A Comprehensive Analysis of Organic Food: Evaluating Nutritional Value and Impact on Human Health.

In recent years, organic agriculture has gained more popularity, yet its approach to food production and its potential impact on consumers' health and various environmental aspects remain to be fully discovered. The goal of organic farming practices is to maintain soil health, sustain ecological systems, maintain fairness in its relationship with the environment and protect the environment in its entirety. Various health benefits have been associated with higher consumption of organic foods. This review identified some of these health benefits, including a reduction in obesity and body mass index (BMI), improvements in blood nutrient composition as well as reductions in maternal obesity and pregnancy-associated preeclampsia risks. Furthermore, organic food consumption can reduce the development of non-Hodgkin lymphoma (NHL) and colorectal cancers. Upon reviewing the existing literature regarding the nutritional value of organic foods, it was found that organic food contained higher levels of iron, magnesium and vitamin C. However, the evidence available to draw definitive causations remains limited due to study biases, short study durations and confounding variables; thus, it cannot be concluded that the organic diet provides any related health benefits. In this review, we provided essential insights and statistical analysis from the evidence available and consider study limitations to evaluate the potential of organic food consumption in positively impacting human health.

Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches.

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.

Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches.

Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3-), chloride (Cl-), phosphate (PO43-), sulfate (SO42-), nitrite (NO2-), nitrate (NO3-), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley's ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the "poor" and "marginal" categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP.

Analysis of the different interventions scenario for programmatic measles control in Bangladesh: A modelling study.

In recent years measles has been one of the most critical public health problem in Bangladesh. Although the Ministry of Health in Bangladesh employs a broad extension of measles control policies, logistical challenges exist, and there is significant doubt regarding the disease burden. Mathematical modelling of measles is considered one of the most effective ways to understand infection transmission and estimate parameters in different countries, such as Bangladesh. In this study, a mathematical modelling framework is presented to explore the dynamics of measles in Bangladesh. We calibrated the model using cumulative measles incidence data from 2000 to 2019. Also, we performed a sensitivity analysis of the model parameters and found that the contact rate had the most significant influence on the basic reproduction number R0. Four hypothetical intervention scenarios were developed and simulated for the period from 2020 to 2035. The results show that the scenario which combines enhanced treatment for exposed and infected population, first and second doses of vaccine is the most effective at rapidly reducing the total number of measles incidence and mortality in Bangladesh. Our findings also suggest that strategies that focus on a single interventions do not dramatically affect the decline in measles incidence cases; instead, those that combine two or more interventions simultaneously are the most effective in decreasing the burden of measles incidence and mortality. In addition, we also evaluated the cost-effectiveness of varying combinations of three basic control strategies including distancing, vaccination and treatment, all within the optimal control framework. Our finding suggested that combines distancing, vaccination and treatment control strategy is the most cost-effective for reducing the burden of measles in Bangladesh. Other strategies can be comprised to measles depending on the availability of funds and policymakers' choices.

Marine waters assessment using improved water quality model incorporating machine learning approaches.

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends on a number of factors, and one of the most important is the quality of the water. The water quality index (WQI) model is widely used to assess water quality, but existing models have uncertainty issues. To address this, the authors introduced two new WQI models: the weight based weighted quadratic mean (WQM) and unweighted based root mean squared (RMS) models. These models were used to assess water quality in the Bay of Bengal, using seven water quality indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), and molybdate reactive phosphorus (MRP). Both models ranked water quality between "good" and "fair" categories, with no significant difference between the weighted and unweighted models' results. The models showed considerable variation in the computed WQI scores, ranging from 68 to 88 with an average of 75 for WQM and 70 to 76 with an average of 72 for RMS. The models did not have any issues with sub-index or aggregation functions, and both had a high level of sensitivity (R2 = 1) in terms of the spatio-temporal resolution of waterbodies. The study demonstrated that both WQI approaches effectively assessed marine waters, reducing uncertainty and improving the accuracy of the WQI score.

Prevalence and determinants of wasting among under-5 Egyptian children: Application of quantile regression.

Wasting is one of the symptoms of malnutrition that has been connected to the deaths of malnourished children. This study was intended to explain the effect of socio-demographic and economic factors on under-5 wasting by evaluating their conditional effect across the distribution of weight-for-height Z (WHZ) scores using the quantile regression (QR) model. The weighted sample which included 13,680 children under 5 years was taken from the countrywide Egyptian DHS 2014 survey. The results depicted that about 2% of Egyptian children were severely wasted, with the prevalence of wasting being around 8%. It was discovered that across the WHZ distribution, the child's features, maternal characteristics, father's education, and social factors had significant but varied contributions in explaining the wasting status of under-5 children. It was revealed that female children had a significant weight advantage, notably 0.21 standard deviation (SD) higher weight at the 95th quantile over their male counterparts. The WHZ score was also found to be significantly positively associated with both age and household's wealth status at the lower and upper tails of the WHZ distribution, respectively. Moreover, in comparison with children whose mothers were underweight, those whose mothers were normal or overweight had higher WHZ scores, with a 1.45 SD increase in WHZ scores at the 95th quantile for mothers who were normal weights. Furthermore, the children who were breastfed, whose mothers received antenatal care (ANC) services, and/or who had educated parents had an advantage in terms of WHZ scores than their counterparts. In addition, the children with higher birth order and/or who resided in urban areas had weight disadvantages compared to their counterparts. Therefore, in order to improve children's nutritional status and achieve the Sustainable Development Goals (SDGs) by 2030, the government and public-private owner organizations must work together at the community level focusing on vulnerable groups.

Burden of COVID-19 on health and wellbeing, education, and economy of Bangladesh.

The long-term impact of this pandemic will continue in almost all sectors of a country such as health, economic situations, education, mental health, and violence. Therefore, the authors intended to discuss the prolonged effect of COVID-19 on the health along with wellbeing, education, and economy of Bangladesh through a mixed approach. To assess the possible scenario in health sector of Bangladesh, we conducted a short survey through online with a structured questionnaire. The impact of the pandemic is highlighted by graphical presentations and discussed the issues in light of the existing literature. It is realized that health care services and resources are always essential for predominant health conditions and fatal diseases patients. Mental health has also been impacted a lot during this pandemic. Moreover, students of only those schools located in urban areas are attended some online classes but due to the internet interruption and scarcity of devices students from rural areas cannot attend the classes and it will widen the gap between pupils from urban areas and those who live in remote rural areas. Additionally, many students terminate school. The country's youth unemployment is projected to intensify as a result of the economic effects, which would promote antisocial behavior and cause social discontent among young people. Also, the flow of remittances greatly declined in the last couple of months and a many people were jobless abroad, and the majority of them were sent back home. The demographic dividend's intended results are expected to be negatively impacted by COVID-19's overall effect as well as sustainable development goals (SDGs) in Bangladesh. Therefore, many social services systems need strategic backup resources at community, national, and global levels if any basic system may collapse due to COVID-19 and socio-economic as well as geopolitical negligence in handling post-pandemic challenges.

A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment.

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components - selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation - with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD5, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer; R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer; R2 = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.

Child marriage and its association with morbidity and mortality of under-5 years old children in Bangladesh.

INTRODUCTION: Child marriage is a significant social and health concern in many low- and middle-income countries (LMICs). This harmful practice violates children's rights and continues to be widespread across developing nations like Bangladesh. This study investigated the mortality trend among Bangladeshi children and the impact of child marriage on under-5 children morbidity and mortality in Bangladesh. METHODS AND MATERIALS: A sample of 8,321 children under-5 years old was analyzed using bivariate and multivariate statistical techniques collected from the recent 2017-18 BDHS data. Chi-square test and logistic regression (unadjusted and adjusted) were used to determine the influence of covariates on the target variable. RESULTS: Results revealed that child mortality was significantly higher among children whose mothers married at an early age than their counterparts. Although the general trend in the prevalence of different childhood mortality in Bangladesh was declining gradually from 1993 to 2018, it was still high in 2018. Also, marriage after 18 years lessens likelihood of diarrhea (adjusted OR = 0.93; 95% CI: 0.76-1.16) and cough (adjusted OR = 0.91; 95% CI: 0.78-1.17) among children. Furthermore, findings reveal that likelihood of different child mortality is higher among early married women. CONCLUSION: Immediate intervention through rigorous enforcement of policies and different programs to raise the age at marriage and by lessening socioeconomic disparities can combat the prevalence of high morbidity and mortality of under-5 years old children. Findings from this study will be helpful to accelerate strategies for achieving the Sustainable Development Goals (SDGs) related to child and maternal health by 2030.

Prevalence and determinants of wife-beating in Bangladesh: evidence from a nationwide survey.

BACKGROUND: Intimate partner violence (IPV) is a global public health concern, with women in low- and middle-income countries (LMICs) bearing a disproportionately high burden. This study investigates the prevalence and factors correlated with attitudes regarding wife-beating among Bangladeshi women in urban-rural contexts. METHODS: A sample of 13,033 urban women and 51,344 rural women data from the Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 were analyzed using the Chi-square test and ordinal logistic regression model. RESULTS: The findings reveal that arguing with her husband is the widespread reason for wife-beating in Bangladesh (urban: 17.3%, rural: 21.9%), followed by neglecting the children (urban: 12.7%, rural: 15.8%). About 8% of urban women and 10% of rural women favoured the opinion that refusing to involve sexual intercourse is a legitimate justification for wife-beating. In comparison, around 5% feel that a husband has a right to beat his wife due to burning food. The respondents' age, education, marital status, number of children, socioeconomic level, any health or physical difficulty, having problems becoming pregnant, and the husband's age are all significant factors in justifying wife-beating. CONCLUSIONS: Bangladesh has a massive challenge in eliminating IPV. Women from lower socioeconomic classes, low levels of education, other challenges, and residents of rural areas are particularly more vulnerable than their urban counterparts. Therefore, it is vital to develop a proper action plan that considers women's education and occupation to raise awareness of the various implications of wife-beating in women, particularly in Bangladesh's rural areas.

Trends and determinants of vaccination among children aged 06-59 months in Bangladesh: country representative survey from 1993 to 2014.

BACKGROUND: Vaccination has important consequences for childhood development, mortality, and inequalities in health and well-being. This research explores the trend of vaccinations coverage from 1993 to 2014 and determines the significant factors for vaccinations coverage in Bangladesh, considering geospatial, socioeconomic, and demographic characteristics. METHODS: This study uses a secondary dataset extracted from the Bangladesh Demographic and Health Survey (BDHS) from 1992 to 93 to 2014. The association between selected independent variables and vaccination coverage of children was examined through the Chi-square test. In addition, unadjusted and adjusted logistic regression approaches were applied to determine the effects of covariates on vaccination status by using the BDHS-2014 dataset. RESULTS: The results reveal that the trend of the vaccination coverage rate has gradually been increased over the study period. The coverage rate of BCG is observed maximum while the lowest for Measles vaccination among all types of vaccinations. The findings revealed that the significantly lower coverage of all vaccination had been observed in the Sylhet region. Children of higher educated mothers (OR 10.21; CI: 4.10-25.37) and father (OR 8.71; CI: 4.03-18.80), born at health facilities (OR 4.53; CI: 2.4-8.55) and whose mother has media exposure (OR 3.20; CI: 2.22-4.60) have more chance of receiving BCG vaccine. For DPT vaccination coverage, there is a significant difference from children whose mothers have primary (OR 1.7; CI: 1.35-2.15), secondary (OR 3.5; CI: 2.75-4.45), and higher (OR 9.6; CI: 5.28-17.42) educational qualification compared to children of illiterate mothers. Findings demonstrated that children born in wealthier households have a higher likelihood of being immunized against DPT, Polio, and Measles vaccination than children born in the poorest households. CONCLUSIONS: The findings reveal that to enhance and make sustainable the overall country's vaccination coverage, we should pay more attention to the mother's education, socioeconomic condition, children's age, birth order number, having media exposure, place of residence, and religion. The authors think that this finding would be helpful to accelerate the achievement target of Sustainable Development Goals (SDGs) for children's health in Bangladesh.

Determinants of exclusive breastfeeding practice in Bangladesh: Evidence from nationally representative survey data.

BACKGROUND: Exclusive breastfeeding (EBF) means that an infant should be breastfed only for the first six months of life to achieve optimal child development and to prevent infant morbidity and mortality. The aim of this analysis was to determine the individual-, household-, and community-level factors associated with EBF practice in Bangladesh. METHODS: A total of 1,440 women-child pairs data were analysed extracted from 2011 and 2014 Bangladesh Demographic and Health Survey. Multilevel logistic regression models were used separately for individual-, household-, and community level factors to identify the different level of factors associated with EBF practice. RESULTS: Around 61% women in Bangladesh practiced EBF with significant variation across several individual-, household-, and community-level factors. At the individual level, higher odds of EBF practice was found among mothers' received higher number of antenatal care and lower age of child. Mothers' higher education and engagement in formal jobs were found negatively associated with EBF practice. At the community level, higher odds of EBF was found among women live in Barishal, Dhaka, and Rajshahi divisions, and resided in the community with moderate level of female education, higher level of fertility, and higher use of antenatal and delivery care. CONCLUSIONS: One in every three children in Bangladesh do not breastfeed exclusively which needs special attention for the policymakers. In this case, educated women engaged in income generating activities and women did not use antenatal care should be given priority. At the community level, priority should be given for the women's resides in the community with lower level of antenatal and delivery healthcare services use.

Jurisdictional, socioeconomic and gender inequalities in child health and development: analysis of a national census of 5-year-olds in Australia.

OBJECTIVES: Early child development may have important consequences for inequalities in health and well-being. This paper explores population level patterns of child development across Australian jurisdictions, considering socioeconomic and demographic characteristics. DESIGN: Census of child development across Australia. SETTING AND PARTICIPANTS: Teachers complete a developmental checklist, the Australian Early Development Index (AEDI), for all children in their first year of full-time schooling. Between May and July 2009, the AEDI was collected by 14 628 teachers in primary schools (government and non-government) across Australia, providing information on 261 147 children (approximately 97.5% of the estimated 5-year-old population). OUTCOME MEASURES: Level of developmental vulnerability in Australian children for five developmental domains: physical well-being, social competence, emotional maturity, language and cognitive skills and communication skills and general knowledge. RESULTS: The results show demographic and socioeconomic inequalities in child development as well as within and between jurisdiction inequalities. The magnitude of the overall level of inequality in child development and the impact of covariates varies considerably both between and within jurisdiction by sex. For example, the difference in overall developmental vulnerability between the best-performing and worst-performing jurisdiction is 12.5% for males and 7.1% for females. Levels of absolute social inequality within jurisdictions range from 8.2% for females to 12.7% for males. CONCLUSIONS: The different mix of universal and targeted services provided within jurisdictions from pregnancy to age 5 may contribute to inequality across the country. These results illustrate the potential utility of a developmental census to shed light on the impact of differences in universal and targeted services to support child development by school entry.