Acute malnutrition and its determinants of preschool children in Bangladesh: gender differentiation.

BACKGROUND: Children acute malnutrition (AM) is a global public health concern, especially in low and middle income countries. AM is associated with multiple physiological vulnerabilities, including immune dysfunction, enteric barrier disruption, gut microbiome dysbiosis, and essential nutrient deficits. This study aimed to determine the prevalence of AM and its associated factors among preschool children in Rajshahi district, Bangladesh. METHODS: This cross-sectional study was conducted from October to December, 2016. Children acute malnutrition was assessed using mid-upper arm circumference. Multiple binary logistic regression analyses were employed to determine the associated factors after adjusting the effect of independent factors of children AM. RESULT: The prevalence of AM amongst preschool children was 8.7%, among them 2.2 and 6.5% were severe acute malnutrition and moderate acute malnutrition, respectively. Z-proportional test demonstrated that the difference in AM between girls (11.6) and boys (5.9%) was significant (p < 0.05). Children AM was associated with being: (i) children aged 6-23 months (aOR = 2.29, 95% CI: 1.20-4.37; p < 0.05), (ii) early childbearing mothers' (age < 20 years) children (aOR = 3.06, 95% CI: 1.08-8.66; p < 0.05), (iii) children living in poor family (aOR = 3.08, 95% CI: 1.11-8.12; p < 0.05), (iv) children living in unhygienic latrine households (aOR = 2.81, 95% CI: 1.52-5.09; p < 0.01), (v) Hindu or other religion children (aOR = 0.42, 95% CI: 0.19-0.92; p < 0.05). CONCLUSION: The prevalence of AM was high among these preschool children. Some modifiable factors were associated with AM of preschool children. Interventions addressing social mobilization and food security could be an effective way to prevent acute malnutrition among children in Bangladesh.

Association of child marriage and nutritional status of mothers and their under-five children in Bangladesh: a cross-sectional study with a nationally representative sample.

BACKGROUND: Child marriage remains an important problem around the world with young mothers and their under-five children often experiencing under-nutrition. The problem is rarely studied in the Bangladeshi population. This paper was designed to identify the association between child marriage and nutritional status of mothers and their under-five children in Bangladesh. METHODS: Nationally representative secondary data was used for this study, data was extracted from the Bangladesh Demographic and Health Survey (BDHS) 2017-18. The sample consisted of 7235 mothers aged 18-49 years and their under-five children. The mothers were classified into two classes according to their age at first marriage: (i) child marriage (marriage at < 18 years) and (ii) not child marriage (marriage at ≥ 18 years). The nutritional status of mothers was measured by body mass index (BMI), and under-five children's nutritional status was measured by (i) height-for-age (z-score) (stunting), (ii) weight-for-age (z-score) (underweight), and (iii) weight-for-height (z-score) (wasting). The chi-square test and two-level logistic regression model were used for data analysis using SPSS software (IBM version 20). RESULTS: The prevalence of child marriage among Bangladeshi women was 69.0%, with the mean and median of age at the first marriage being 16.57 ± 2.83 years and 16 years, respectively. Of the mothers, 15.2% suffered from chronic energy deficiency (underweight), and 72.8% were married at < 18 years. The prevalence of stunting, underweight, and wasting among under-five children in Bangladesh was 31.0%, 22.0%, and 8.5%, respectively. Compared to women married at the age of ≥ 18 years, there was a significantly higher likelihood of chronic energy deficiency among women who married at < 18 years [Adjusted OR = 1.27, CI: 1.05-1.82; p < 0.05]. Under-five children of mothers married before the age of 18 were more likely to have stunting [Adjusted OR = 1.201, CI: 1.11-1.72; p < 0.05], wasting [Adjusted OR = 1.519, CI: 1.15-2.00; p < 0.01], and underweight [Adjusted OR = 1.150, CI: 1.09-1.82; p < 0.05] compared to children of mothers who married at age ≥ 18. CONCLUSION: The rate of child marriage among Bangladeshi women is high, and it is significantly associated with malnutrition among mothers and their under-five children. The Bangladesh government can use the findings of this study to prevent and reduce child marriage and malnutrition among mothers and their under-five children to achieve sustainable development goals by 2030.

Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study.

Psychological and behavioral stress has increased enormously during Coronavirus Disease 2019 (COVID-19) pandemic. However, early prediction and intervention to address psychological distress and suicidal behaviors are crucial to prevent suicide-related deaths. This study aimed to develop a machine algorithm to predict suicidal behaviors and identify essential predictors of suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic. An anonymous online survey was conducted among university students in Bangladesh from June 1 to June 30, 2022. A total of 2391 university students completed and submitted the questionnaires. Five different Machine Learning models (MLMs) were applied to develop a suitable algorithm for predicting suicidal behaviors among university students. In predicting suicidal behaviors, the most crucial background and demographic features were relationship status, friendly environment in the family, family income, family type, and sex. In addition, features related to the impact of the COVID-19 pandemic were identified as job loss, economic loss, and loss of family/relatives due to COVID-19. Moreover, factors related to mental health include depression, anxiety, stress, and insomnia. The performance evaluation and comparison of the MLM showed that all models behaved consistently and were comparable in predicting suicidal risk. However, the Support Vector Machine was the best and most consistent performing model among all MLMs in terms of accuracy (79%), Kappa (0.59), receiver operating characteristic (0.89), sensitivity (0.81), and specificity (0.81). Support Vector Machine is the best-performing model for predicting suicidal risks among university students in Bangladesh and can help in designing appropriate and timely suicide prevention interventions.

Two level logistic regression analysis of factors influencing skilled birth attendant during delivery among Bangladeshi mothers: A nationally representative sample.

BACKGROUND: In order to minimize the maternal and child mortality rate, the presence of skilled birth attendants (SBA) during delivery is essential. By 2022, 4th health, population and nutrition sector programme in Bangladesh aims to increase the percentage of deliveries performed by SBA to 65 percent. The objective of the present study was to determine the rate and associated factors of usage SBA among Bangladeshi mothers during their delivery. METHODS: This study utilized secondary data that was collected by Bangladesh Demographic and Health Survey (BDHS) 2017-18. The usage of SBA was measured by a question to respondent, who assisted during your delivery? It was classified into two classes; (i) skilled birth attendant (qualified doctors, nurses, midwives, or paramedics; family welfare visitors, community skilled birth attendants, and sub-assistant community medical officers) (code 1), and (ii) unskilled birth attendant (untrained traditional birth attendants, trained traditional birth attendants, relatives, friends, or others) (code 0). Two logistic regression model was used to determine the associated factors of SBA after removing the cluster effect of the outcome variable. RESULTS: This study found 53.2% mothers were delivered by SBA in Bangladesh, among them 56.33% and 42.24% mothers were delivered by nurse/midwife/paramedic and doctor respectively. The two level logistic model demonstrated that geographical location (division), type of residence, religion, wealth index, mothers' body mass index, mothers' education level, mothers' occupation, total ever born children, mothers' age at first birth (year), number of ANC visits, husbands' education level and husbands' occupation were significant (p<0.01) predictors of SBA. Mothers' education and wealth index were the most important contributory factors for SBA in Bangladesh. CONCLUSIONS: This study revealed that still 46.8% mothers are delivered by unskilled birth attendant, this might be treated of Bangladesh Government to achieve SDGs indicator 3.1.2 by 2030. Counseling could be integrated during ANC to increase awareness, and should ensure for every Bangladeshi mothers visit ANC service during their pregnancy at least 4 times.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity.

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

Nutritional status of tribal and non-tribal adults in rural Bangladesh: A comparative study.

BACKGROUND: Nutritional status is an important indicator of health status among adults. However, to date, there exists scanty information on the nutritional status of tribal populations of Bangladesh. The aim of the study was to investigate the nutritional status of tribal (T) and non-tribal (NT) adult people living in the rural area of Rajshahi district, Bangladesh. METHODS: A total of 420 (72 T and 348 NT) households were studied. The samples were selected using multistage stratified sampling with proportional allocation. The nutritional status of adults was measured using body mass index (BMI). Descriptive statistics, t-test, ANOVA and Z-proportional test were utilized for data analysis. RESULTS: The study revealed that 8.3% and 9.2% of T and NT men were suffering from under nutrition respectively, while the corresponding figures in women were 12.5% and 10.1% respectively. Overall, 11.1% and 27.0% men, and 13.9% and 29.3% women T and NT were over-nourished respectively. The rate of over nutrition among T was significantly (p<0.05) higher than NT for both sexes. The mean weight and BMI of the NT men were significantly (p<0.01) higher than T men. The mean weight, height and BMI of NT women were higher (p<0.05) than T women. ANOVA demonstrated that the variation in BMI among education levels of NT men and the variation among occupation for both ethnicities were significant (p<0.01). The variation in BMI among education levels and occupation of T and NT women were significant (p<0.05), moreover ordinal logistic regression model demonstrated that hygienic toilet facilities and father's occupation were predictors of nutritional status. The interaction effects of education and occupation, and education and household monthly income on BMI were significant (p<0.01) for T men and both T and NT women (p<0.05). CONCLUSIONS: The prevalence of over-nutrition among NT is higher than T for both sexes. Some socio-economic and demographic factors were found as predictors of malnutrition. At least 12 of the 17 Sustainable Development Goals (SDGs) contain indicators that are highly related to nutrition, our findings can help Bangladesh Government for achieving SDGs by 2030. Appropriate nutritional intervention and awareness programmes can be initiated by the Government to ameliorate the burden of malnutrition among adults in the country.

Application of a count regression model to identify the risk factors of under-five child morbidity in Bangladesh.

BACKGROUND: Bangladesh has seen a significant decline in child mortality in recent decades, but morbidity among children <5 y of age remains high. The aim of this analysis was to examine trends and identify risk factors related to child morbidity in Bangladesh. METHODS: This analysis is based on data from four successive cross-sectional Bangladesh Demographic and Health Surveys for the years 2007, 2011, 2014 and 2017-18. Several count regression models were fitted and the best model was used to identify risk factors associated with morbidity in children <5 y of age. RESULTS: According to the results of the trend analysis, the prevalence of non-symptomatic children increased and the prevalence of fever, diarrhoea and acute respiratory infections (ARIs) decreased over the years. The Vuong's non-nested test indicated that Poisson regression could be used as the best model. From the results of the Poisson regression model, child age, sex, underweight, wasted, stunting, maternal education, wealth status, religion and region were the important determinants associated with the risk of child morbidity. The risk was considerably higher among women with a primary education compared with women with a secondary or greater education in Bangladesh. CONCLUSIONS: This analysis concluded that child morbidity is still a major public health problem for Bangladesh. Thus it is important to take the necessary measures to reduce child morbidity (particularly fever, diarrhoea and ARI) by improving significant influencing factors.

Virtual diabetic patient with physical activity dynamics.

BACKGROUND AND OBJECTIVE: Diabetes is a disease of impaired blood glucose regulation due to the absence or insufficient secretion of insulin hormone or insulin resistance induced in the human body. In literature, the impact of exercise is considered in few models based on the minimal representation of glucose dynamics along with the assumption that no endogenous insulin is produced in the body. Hence these models are not capable of describing diabetic behavior which is independent of exogenous insulin. This type of diabetes, known as type-2, affects almost 90% of the total diabetes population. In this article, a constraint-based comprehensive physiological model of blood glucose dynamics is aimed to build for filling up the gap in the literature. METHODS: For physiological comprehensiveness, the model is considered to consist of several compartments separately connected with a common compartment named 'plasma'. Plasma is the only accessible compartment and contains the state variables. Plasma variables are the integrated result of the net change in rates of metabolic processes and basal rates are influenced between two saturation constraints for an operating range of each plasma variable. The influence of a plasma variable on a metabolic rate is represented using a form of the hyperbolic tangent function. Validation is done by fitting the model with clinical experiments and continuous glucose monitoring data of a free-living environment. RESULTS: The proposed model generates an average correlation coefficient of 0.85 ± 0.13 on all simulated responses with the target in the fitting experiments. Besides this, the model can produce a spectrum of metabolic effects of plasma variables for showing more insight into glucose metabolism. CONCLUSIONS: A constraint-based comprehensive glucose regulation with exercise dynamics for modeling diabetes is pursued. The model doesn't consider age, gender, physical, and mental condition of the human body but can be applied in operation research by mathematical programming.

Determinants of Early Initiation of Breastfeeding Practices of Newborns in Bangladesh: Evidence From Bangladesh Demographic and Health Survey.

PURPOSE: Early initiation of breastfeeding is essential for newborns after birth to reduce mortality and morbidity. Early initiation of breastfeeding awareness/activities may be a vital role in Bangladesh to minimize the infant deaths. The aim of this study is to identify factors associated with the early initiation of breastfeeding practices. METHODS: In this study, Bangladesh Demographic and Health Survey (BDHS) 2017 to 2018 data was used that will be the first analysis for early initiation of breastfeeding practices in this data set in Bangladesh. Considering the importance of early breastfeeding practices, the dependent variable was divided into 3 categories (immediately: breastfeeding for less than 20 minutes, within an hour, and after 1 hour) to find a significant association with early breastfeeding practices in Bangladesh. Bivariate analysis is used to examine the differentials to early initiation of breastfeeding according to the selected number of background variables. Multinomial logistic regression is used to determine predictive independent factors associated with the dependent variable. RESULTS: Using BDHS 2017 to 2018 data on 4950 observations, this study revealed that 24.6% of mothers breastfed their babies immediately after birth and 36.2% of mothers breastfed their babies within an hour. The rate of mothers who breastfeed their babies immediately after birth is lowest at the age of 20 to 25, mothers with a higher level of education, richer class, Khulna division, the first child born, Islam, and private/NGO. With a multivariate analysis of breastfeeding within an hour compared to immediate breastfeeding: richest (OR = 0.71), Barisal division (OR = 0.72), and Buddhism ( O R = 0 . 52 ) are less likely to breastfeed newborns compared to the reference category. On the other hand, primary, secondary, and higher educated mothers are more likely to breastfeed newborns compared to no educated mothers. Besides, breastfeeding newborns after 1 hour compared to immediate after birth: mothers aged 20 to 25 (OR = 1.40), richer (OR = 1.46), higher secondary (OR = 2.06), Khulna division (OR = 1.81), and private/NGO (OR = 2.51) are more likely breastfeed newborn. CONCLUSION: Mother's education, wealth index, region, birth order, religion, and place of delivery have a significant impact on the early initiation of breastfeeding practices, but the rate of immediate breastfeeding is relatively lower than others. Ultimately, this information will help planners and other professionals plan strategies and interventions to provide good quality health services.

Assessment of Heavy Metal Concentration in Water, Sediment and Common Fish Species of Dhaleshwari River in Bangladesh and their Health Implications.

The present study emphasized on evaluating the extent of pollution of Dhaleshwari River in Bangladesh due to the discharge of heavy metals from tanneries and other industries along with the health risks associated with the consumption of the heavy metals accumulated fish. For this purpose, three spots of Dhaleshwari River which are in the vicinity of the industrial outlet were selected for evaluating the seasonal status of heavy metals in water, sediment, and organs of three common fish species. Average concentrations of metals in water and sediment were in the order of Cr > Cd > Pb > Cu > As and Cr > Pb > Cu > As > Cd respectively. The average HM concentrations in water and sediment exceeded WHO and USEPA standards suggesting serious pollution to the aquatic environment. In fish organs, metal concentrations were in the order of Cu > Cr > Pb > Cd > As. Accumulation was highest in gills and lowest in muscles. Fish muscles had a relatively higher concentration of heavy metals (except As) exceeding the safe limits of FAO and WHO. Seasonal variation was also observed in water for all metals (p < 0.01), in sediment for Cu and As (p < 0.05), and in fish for Cr, Cd, and Cu (p < 0.05); higher concentrations were observed in winter. Bioconcentration factor analysis indicated that Cu, Pb, and Cr were more concentrated in fish. Health risk assessment reveals that the carcinogenic risk of Cr is associated with the consumption of contaminated fish species of the studied area.

SICE: an improved missing data imputation technique.

In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeholders, efficiently handling missing values becomes more important. In this paper, we have proposed a new technique for missing data imputation, which is a hybrid approach of single and multiple imputation techniques. We have proposed an extension of popular Multivariate Imputation by Chained Equation (MICE) algorithm in two variations to impute categorical and numeric data. We have also implemented twelve existing algorithms to impute binary, ordinal, and numeric missing values. We have collected sixty-five thousand real health records from different hospitals and diagnostic centers of Bangladesh, maintaining the privacy of data. We have also collected three public datasets from the UCI Machine Learning Repository, ETH Zurich, and Kaggle. We have compared the performance of our proposed algorithms with existing algorithms using these datasets. Experimental results show that our proposed algorithm achieves 20% higher F-measure for binary data imputation and 11% less error for numeric data imputations than its competitors with similar execution time.

Early age at menarche and its associated factors in school girls (age, 10 to 12 years) in Bangladesh: a cross-section survey in Rajshahi District, Bangladesh.

BACKGROUND: Early onset of menarche is one of the most important factors for breast cancer and other associated health hazards. The aim of this study was to investigate the early age at menarche and its associated factors in school girls (age, 10-12 years) in Rajshahi District, Bangladesh. METHODS: Data was collected from Rajshahi District, Bangladesh, using multistage random sampling. Independent sample t test and binary logistic regression model were used in this study. A total number of 386 school girls aged 10-12 years were considered as a sample for this study. RESULTS: This study revealed that more than 48% girls already attained menarche within the age of 12 years, among them 25.6%, 41.0%, and 58.3% girls experienced menarche at the age of 10, 11, and 12 years, respectively. It was observed that the menarcheal girls were significantly taller (p < 0.01) and heavier (p < 0.01) than non-menarcheal girls. The menarcheal girls' mothers were heavier (p < 0.01), shorter (p < 0.01), had more BMI (p < 0.01), reached menarche (p < 0.05) earlier than non-menarcheal girls' mothers. Menarcheal girls had less number of siblings (p < 0.01) and lower order of birth (p < 0.05) than non-menarcheal girls. After controlling the effect of other factors, multiple logistic regression model demonstrated that obese girls were more likely to attain menarche than under- [AOR = 0.279, CI 95% 0.075-0.986; p < 0.05] and normal [AOR = 0.248, CI 95% 0.082-0.755; p < 0.05] weight girls. Urban school girls had more chance to get menarche than rural school girls at same age (AOR = 0.012, 95% CI 0.003-0.047; p < 0.01). CONCLUSIONS: Therefore, modern lifestyle changes may have the important factors for early age at menarche of the studied girls in Bangladesh.