Waste collectors face multi-faceted health risks including exposure to musculoskeletal disorders, respiratory diseases, and workplace injuries because of their hazardous work environment. The purpose of this study was to determine the risk factors that affect health of the waste collectors across Bangladesh. Data on a cross-sectional survey obtained information from 481 waste collectors about their demographics, housing conditions, hygiene practices, security measures, and disease prevalence. Descriptive analyses and multi-level models are used. As per univariate analysis, 81.3% did not have access to clean water, 58.4% did not have access to sanitation, and 65.9% of people lived in unhealthy housing. According to bivariate analysis, there is an association between unhealthy working conditions i.e., respiratory illnesses (19.1% asthma, 29.7% cough), gastrointestinal issues (59.3% discomfort, 24.1% diarrhea), musculoskeletal disorders, and dermatological diseases, and unsafe working conditions (i.e., 60% no masks, 71% no gloves, and 75% no boots). Considering geographic clustering, multi-level modeling examined how different factors affected particular illnesses. The following were significant protective factors: better housing (80% lower odds of acute irritation), security measures (50% lower odds of gastrointestinal diseases), good hygiene (62% lower odds of dermatological diseases), and younger age (2% higher odds of respiratory diseases per year). In summary, unsafe living and working conditions greatly push the risks of illness for waste collectors. These risks to occupational health can be reduced with targeted interventions that enhance housing, hygiene, security protocols, and working conditions.

Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fasting blood glucose levels, that result in an increased risk of heart disease and stroke. In this research, we aim to identify the risk factors that have an impact on MetS in the Bangladeshi population. Subsequently, we intend to construct predictive machine learning (ML) models and ultimately, assess the accuracy and reliability of these models. In this particular study, we utilized the ATP III criteria as the basis for evaluating various health parameters from a dataset comprising 8185 participants in Bangladesh. After employing multiple ML algorithms, we identified that 27.8% of the population exhibited a prevalence of MetS. The prevalence of MetS was higher among females, accounting for 58.3% of the cases, compared to males with a prevalence of 41.7%. Initially, we identified the crucial variables using Chi-Square and Random Forest techniques. Subsequently, the obtained optimal variables are employed to train various models including Decision Trees, Random Forests, Support Vector Machines, Extreme Gradient Boosting, K-nearest neighbors, and Logistic Regression. Particularly we employed the ATP III criteria, which utilizes the Waist-to-Height Ratio (WHtR) as an anthropometric index for diagnosing abdominal obesity. Our analysis indicated that Age, SBP, WHtR, FBG, WC, DBP, marital status, HC, TGs, and smoking emerged as the most significant factors when using Chi-Square and Random Forest analyses. However, further investigation is necessary to evaluate its precision as a classification tool and to improve the accuracy of all classifiers for MetS prediction.

The stage of pregnancy is crucial for women of reproductive age and their families. However, in low- and middle-income countries like Bangladesh, antenatal and postnatal care are not widely practiced due to various socio-economic factors, such as low education levels, income, age, pregnancy knowledge, and limited healthcare facilities. The objective of this study was to examine the factors associated with antenatal care in two locations in Bangladesh based on the data collected from the Bangladesh Demographic and Health Survey (BDHS) 2017–2018. We explored different variables as explanatory variables related to ANC service. The results showed that most of the respondents were from rural areas, with 77.02% receiving antenatal care at home. Women with secondary education were more likely to receive care at home than those without education. The Chi-square test indicated a positive correlation between antenatal care at home with several variables, whereas, in the case of Upazila health complexes, only three variables showed a positive association. Logistic regression analysis further showed some specific variables such as geographical division, religion, iron intake during pregnancy, and reporting pregnancy complications had a significant impact on ANC at home. In contrast, covariates such as residence, division, and wealth index were significant for antenatal care at Upazila health complexes. The division was a significant covariate in both cases. Interestingly, we observed that mothers who had been informed about the signs of pregnancy complications were 92% more likely to receive antenatal care at home than those who had not experienced pregnancy complications. Conversely, the results revealed that mothers who were unaware of pregnancy complications were 32% more likely to receive antenatal care at home than those who had been informed about complications. This suggests that when women are educated about pregnancy complications, they are more likely to receive more antenatal care. However, Bangladesh's situation is quite different due to a lack of proper education and knowledge of antenatal care services.

Predicting mortality in COVID‐19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID‐19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R² and root‐mean‐square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author’s analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario.