Enhanced classification of left ventricular hypertrophy in cardiac patients using extended Siamese CNN.

OBJECTIVE: Left Ventricular Hypertrophy (LVH) is the thickening of the left ventricle wall of the heart. The objective of this study is to develop a novel approach for the accurate assessment of Left Ventricular Hypertrophy (LVH) severity, addressing the limitations of traditional manual grading systems. Approach: We propose the Multi-purpose Siamese Weighted Euclidean Distance Model (MSWED), which utilizes convolutional Siamese neural networks and zero-shot/few-shot learning techniques. Unlike traditional methods, our model introduces a cutoff distance-based approach for zero-shot learning, enhancing accuracy. We also incorporate a weighted Euclidean distance targeting informative regions within echocardiograms. Main Results: We collected comprehensive datasets labeled by experienced echocardiographers, including Normal heart and various levels of LVH severity. Our model outperforms existing techniques, demonstrating significant precision enhancement, with improvements of up to 13\% for zero-shot and few-shot learning approaches. Significance: Accurate assessment of LVH severity is crucial for clinical prognosis and treatment decisions. Our proposed MSWED model offers a more reliable and efficient solution compared to traditional grading systems, reducing subjectivity and errors while providing enhanced precision in severity classification.

After-meal blood glucose level prediction for type-2 diabetic patients.

Type 2 Diabetes, a metabolic disorder disease, is becoming a fast growing health crisis worldwide. It reduces the quality of life, and increases mortality and health care costs unless managed well. After-meal blood glucose level measure is considered as one of the most fundamental and well-recognized steps in managing Type 2 diabetes as it guides a user to make better plans of their diet and thus control the diabetes well. In this paper, we propose a data-driven approach to predict the 2 h after meal blood glucose level from the previous discrete blood glucose readings, meal, exercise, medication, & profile information of Type 2 diabetes patients. To the best of our knowledge, this is the first attempt to use discrete blood glucose readings for 2 h after meal blood glucose level prediction using data-driven models. In this study, we have collected data from five prediabetic and diabetic patients in free living conditions for six months. We have presented comparative experimental study using different popular machine learning models including support vector regression, random forest, and extreme gradient boosting, and two deep layer techniques: multilayer perceptron, and convolutional neural network. We present also the impact of different features in blood glucose level prediction, where we observe that meal has some modest and medication has a good influence on blood glucose level.

Predictive performance of machine learning compared to statistical methods in time-to-event analysis of cardiovascular disease: a systematic review protocol.

BACKGROUND: Globally, cardiovascular disease (CVD) remains the leading cause of death, warranting effective management and prevention measures. Risk prediction tools are indispensable for directing primary and secondary prevention strategies for CVD and are critical for estimating CVD risk. Machine learning (ML) methodologies have experienced significant advancements across numerous practical domains in recent years. Several ML and statistical models predicting CVD time-to-event outcomes have been developed. However, it is not known as to which of the two model types-ML and statistical models-have higher discrimination and calibration in this regard. Hence, this planned work aims to systematically review studies that compare ML with statistical methods in terms of their predictive abilities in the case of time-to-event data with censoring. METHODS: Original research articles published as prognostic prediction studies, which involved the development and/or validation of a prognostic model, within a peer-reviewed journal, using cohort or experimental design with at least a 12-month follow-up period will be systematically reviewed. The review process will adhere to the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. ETHICS AND DISSEMINATION: Ethical approval is not required for this review, as it will exclusively use data from published studies. The findings of this study will be published in an open-access journal and disseminated at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42023484178.

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.

Adverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities. To address these limitations, we propose a node embedding-based graph outlier detection algorithm to predict adverse pregnancy outcomes. We developed a knowledge graph using a well-curated representative dataset of the Emirati population and two node embedding algorithms. The graph autoencoder (GAE) was trained by applying a combination of original risk factors and node embedding features. Samples that were difficult to reconstruct at the output of GAE were identified as outliers considered representing PTB and LBW samples. Our experiments using LBW, PTB, and very PTB datasets demonstrated that incorporating node embedding considerably improved performance, achieving a 12% higher AUC-ROC compared to traditional GAE. Our study demonstrates the effectiveness of node embedding and graph outlier detection in improving the prediction performance of adverse pregnancy outcomes in well-curated population datasets.

A data-efficient zero-shot and few-shot Siamese approach for automated diagnosis of left ventricular hypertrophy.

Left ventricular hypertrophy (LVH) is a life-threatening condition in which the muscle of the left ventricle thickens and enlarges. Echocardiography is a test performed by cardiologists and echocardiographers to diagnose this condition. The manual interpretation of echocardiography tests is time-consuming and prone to errors. To address this issue, we have developed an automated LVH diagnosis technique using deep learning. However, the availability of medical data is a significant challenge due to varying industry standards, privacy laws, and legal constraints. To overcome this challenge, we have proposed a data-efficient technique for automated LVH classification using echocardiography. Firstly, we collected our own dataset of normal and LVH echocardiograms from 70 patients in collaboration with a clinical facility. Secondly, we introduced novel zero-shot and few-shot algorithms based on a modified Siamese network to classify LVH and normal images. Unlike traditional zero-shot learning approaches, our proposed method does not require text vectors, and classification is based on a cutoff distance. Our model demonstrates superior performance compared to state-of-the-art techniques, achieving up to 8% precision improvement for zero-shot learning and up to 11% precision improvement for few-shot learning approaches. Additionally, we assessed the inter-observer and intra-observer reliability scores of our proposed approach against two expert echocardiographers. The results revealed that our approach achieved better inter-observer and intra-observer reliability scores compared to the experts.

Cardiac phase detection in echocardiography using convolutional neural networks.

Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two cardiac phases-end-systolic (ES) and end-diastolic (ED)-which are critical for calculating heart chamber size and ejection fraction. However, non-essential frames called Non-ESED frames may appear between these phases. Currently, technicians or cardiologists manually detect these phases, which is time-consuming and prone to errors. To address this, an automated and efficient technique is needed to accurately detect cardiac phases and minimize diagnostic errors. In this paper, we propose a deep learning model called DeepPhase to assist cardiology personnel. Our convolutional neural network (CNN) learns from echocardiography images to identify the ES, ED, and Non-ESED phases without the need for left ventricle segmentation or electrocardiograms. We evaluate our model on three echocardiography image datasets, including the CAMUS dataset, the EchoNet Dynamic dataset, and a new dataset we collected from a cardiac hospital (CardiacPhase). Our model outperforms existing techniques, achieving 0.96 and 0.82 area under the curve (AUC) on the CAMUS and CardiacPhase datasets, respectively. We also propose a novel cropping technique to enhance the model's performance and ensure its relevance to real-world scenarios for ES, ED, and Non ES-ED classification.

Phytochemical and Biological Investigation of an Indigenous Plant of Bangladesh, Gynura procumbens (Lour.) Merr.: Drug Discovery from Nature.

Gynura procumbens (Lour.) Merr. (Family: Asteraceae) is a tropical Asian medicinal plant found in Thailand, China, Malaysia, Indonesia, and Vietnam. It has long been utilized to treat a variety of health concerns in numerous countries around the world, such as renal discomfort, constipation, diabetes mellitus, rheumatism, and hypertension. The chemical investigation resulted in the isolation and characterization of six compounds from the methanol (MeOH) extract of the leaves of Gynura procumbens, which were identified as phytol (1), lupeol (2), stigmasterol (3), friedelanol acetate (4), ß-amyrin (5), and a mixture of stigmasterol and ß-sitosterol (6). In-depth investigations of the high-resolution 1H NMR and 13C NMR spectroscopic data from the isolated compounds, along with comparisons to previously published data, were used to clarify their structures. Among these, the occurrence of Compounds 1 and 4 in this plant are reported for the first time. The crude methanolic extract (CME) and its different partitionates, i.e., petroleum ether (PESF), chloroform (CSF), ethyl acetate (EASF), and aqueous (AQSF) soluble fractions, were subjected to antioxidant, cytotoxic, thrombolytic, and anti-diabetic activities. In a DPPH free radical scavenging assay, EASF showed the maximum activity, with an IC50 value of 10.78 µg/mL. On the other hand, CSF displayed the highest cytotoxic effect with an LC50 value of 1.94 µg/mL compared to 0.464 µg/mL for vincristine sulphate. In a thrombolytic assay, the crude methanolic extract exhibited the highest activity (63.77%) compared to standard streptokinase (70.78%). During the assay for anti-diabetic activity, the PESF showed 70.37% of glucose-lowering activity, where standard glibenclamide showed 63.24% of glucose-reducing activity.

Deep learning approach to security enforcement in cloud workflow orchestration.

Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients' data managed by a workflow deployed on the cloud need to be protected, and communication of such data across multiple stakeholders should also be secured. In general, security threats in cloud environments have been studied extensively. Such threats include data breaches, data loss, denial of service, service rejection, and malicious insiders generated from issues such as multi-tenancy, loss of control over data and trust. Supporting the security of a cloud workflow deployed and executed over a dynamic environment, across different platforms, involving different stakeholders, and dynamic data is a difficult task and is the sole responsibility of cloud providers. Therefore, in this paper, we propose an architecture and a formal model for security enforcement in cloud workflow orchestration. The proposed architecture emphasizes monitoring cloud resources, workflow tasks, and the data to detect and predict anomalies in cloud workflow orchestration using a multi-modal approach that combines deep learning, one class classification, and clustering. It also features an adaptation scheme to cope with anomalies and mitigate their effect on the workflow cloud performance. Our prediction model captures unsupervised static and dynamic features as well as reduces the data dimensionality, which leads to better characterization of various cloud workflow tasks, and thus provides better prediction of potential attacks. We conduct a set of experiments to evaluate the proposed anomaly detection, prediction, and adaptation schemes using a real COVID-19 dataset of patient health records. The results of the training and prediction experiments show high anomaly prediction accuracy in terms of precision, recall, and F1 scores. Other experimental results maintained a high execution performance of the cloud workflow after applying adaptation strategy to respond to some detected anomalies. The experiments demonstrate how the proposed architecture prevents unnecessary wastage of resources due to anomaly detection and prediction.

Infant Low Birth Weight Prediction Using Graph Embedding Features.

Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms.

Accurate prediction of a newborn's birth weight (BW) is a crucial determinant to evaluate the newborn's health and safety. Infants with low BW (LBW) are at a higher risk of serious short- and long-term health outcomes. Over the past decade, machine learning (ML) techniques have shown a successful breakthrough in the field of medical diagnostics. Various automated systems have been proposed that use maternal features for LBW prediction. However, each proposed system uses different maternal features for LBW classification and estimation. Therefore, this paper provides a detailed setup for BW estimation and LBW classification. Multiple subsets of features were combined to perform predictions with and without feature selection techniques. Furthermore, the synthetic minority oversampling technique was employed to oversample the minority class. The performance of 30 ML algorithms was evaluated for both infant BW estimation and LBW classification. Experiments were performed on a self-created dataset with 88 features. The dataset was obtained from 821 women from three hospitals in the United Arab Emirates. Different performance metrics, such as mean absolute error and mean absolute percent error, were used for BW estimation. Accuracy, precision, recall, F-scores, and confusion matrices were used for LBW classification. Extensive experiments performed using five-folds cross validation show that the best weight estimation was obtained using Random Forest algorithm with mean absolute error of 294.53 g while the best classification performance was obtained using Logistic Regression with SMOTE oversampling techniques that achieved accuracy, precision, recall and F1 score of 90.24%, 87.6%, 90.2% and 0.89, respectively. The results also suggest that features such as diabetes, hypertension, and gestational age, play a vital role in LBW classification.

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data.

Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one's opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either 'human' or 'bot.' We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework 'DeeProBot,' which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.

Vaccine versus Variants (3Vs): Are the COVID-19 Vaccines Effective against the Variants? A Systematic Review.

BACKGROUND: With the emergence and spread of new SARS-CoV-2 variants, concerns are raised about the effectiveness of the existing vaccines to protect against these new variants. Although many vaccines were found to be highly effective against the reference COVID-19 strain, the same level of protection may not be found against mutation strains. The objective of this study is to systematically review relevant studies in the literature and compare the efficacy of COVID-19 vaccines against new variants. METHODS: We conducted a systematic review of research published in Scopus, PubMed, and Google Scholar until 30 August 2021. Studies including clinical trials, prospective cohorts, retrospective cohorts, and test negative case-controls that reported vaccine effectiveness against any COVID-19 variants were considered. PRISMA recommendations were adopted for screening, eligibility, and inclusion. RESULTS: 129 unique studies were reviewed by the search criteria, of which 35 met the inclusion criteria. These comprised of 13 test negative case-control studies, 6 Phase 1-3 clinical trials, and 16 observational studies. The study location, type, vaccines used, variants considered, and reported efficacies were highlighted. CONCLUSION: Full vaccination (two doses) offers strong protection against Alpha (B.1.1.7) with 13 out of 15 studies reporting more than 84% efficacy. The results are not conclusive against the Beta (B.1.351) variant for fully vaccinated individuals with 4 out of 7 studies reporting efficacies between 22 and 60% and 3 out of 7 studies reporting efficacies between 75 and 100%. Protection against Gamma (P.1) variant was lower than 50% according to two studies in fully vaccinated individuals. The data on Delta (B.1.617.2) variant is limited but indicates lower protection compared to other variants.

Impact of child's cognitive and social-emotional difficulties on child abuse: Does mother's justification of intimate partner violence also play a role?

BACKGROUND: Violence against children has been a persistent problem in developing nations. The adverse effects of physical violence bear a considerable impact on children's physical and psychological development resulting in both short and long-term issues. OBJECTIVE: The aim of this study was to explore whether children with cognitive and social-emotional difficulties (CSEDs) were at a higher risk of experiencing physical abuse and whether mothers' views on intimate partner violence (IPV) were also related to physical abuse against children. PARTICIPANTS AND SETTING: The Bangladesh Multiple Indicator Cluster Survey-2019 was used with a sample of 27,086 children aged 5-14. METHODS: Generalized linear modelling along with a machine learning method of classification trees was employed to investigate the important sociodemographic characteristics and identify the most vulnerable groups of children based on their likelihood of exposure to household-violence. RESULTS: Nearly 62.5 % of the children were physically abused by their mothers. Children with CSEDs were 53 % (OR 1.53; 95 % CI: 1.41, 1.67) more likely to experience physical abuse and mothers' justification of IPV was associated with a 16 % higher risk (OR 1.16; 95 % CI: 1.08, 1.26). Moreover, younger children aged 11 or below belonged to the high-risk groups of experiencing abuse. CONCLUSIONS: The findings suggest that violence against children is widespread in Bangladesh, especially in children having CSEDs. Mothers' acceptance of IPV was also associated with increased abusive practice against children. Sincere focus on these issues is imperative if Bangladesh intends to achieve the sustainable development goal 16.2 of eradicating all forms of violence against children and ensure their safe development.

Indole alkaloids from the leaves of Ravenia spectabilis engl. with activity against pancreatic cancer cell line.

Two previously undescribed indole alkaloids, 3-prenyl-5(3-keto-but-1-enyl) indole and 3-prenyl-indole-5-carbaldehyde, the structurally-related 3,5-diprenyl indole and four known alkaloids were isolated from the leaves of Ravenia spectabilis Engl. Structures were elucidated based on nuclear magnetic resonance (1D and 2D NMR) spectroscopic and mass spectrometric analysis. The previously undescribed compounds isolated were subsequently screened against the HeLa (human cervical cancer), MIA PaCa-2 (human pancreatic adenocarcinoma) and A549 (lung cancer) cell lines. Among the isolated compounds, 3,5-diprenyl indole was the most cytotoxic across all three cell lines (MIA PaCa-2 IC50 = 9.5 ± 2.2 µM). Molecular modelling studies suggested DNA intercalation as the mode of action of these compounds.

Association between malnutrition and anemia in under-five children and women of reproductive age: Evidence from Bangladesh Demographic and Health Survey 2011.

BACKGROUND: Bangladesh is one of the most anemia prone countries in South Asia. Children of age under five years and women of reproductive age are particularly vulnerable in this region. Although several studies have investigated the risk factors of anemia, only few have explored its association with malnutrition, despite its high prevalence in the same group. The objective of this paper is to investigate the association of malnutrition with anemia by conducting separate analyses for under-five children and women of reproductive age using data from the nationally representative 2011 Bangladesh Demographic and Health Survey. METHODS: Two binary outcome variables are considered separately: presence of anemia in children under five years of age (Hb<11.0 g/dl) and presence of anemia in women of childbearing age (Hb<12.0 g/dl). The exposures of interest corresponding to these two outcomes are stunting (low height-for-age) and low BMI (<18.5 kg/m2), respectively. Preliminary analysis involves estimating the association between exposure and outcome while controlling for a single confounder by computing adjusted odds ratios (adjOR) using the Cochran-Mantel-Haenszel approach in stratified analysis. Later, associations between the exposures and outcomes are estimated separately for under-five children and women of reproductive age by fitting multivariable regression models that adjust simultaneously for several confounders. RESULTS: The prevalence of anemia is found to be higher among both the stunted children and women with low BMI compared to their healthy counterparts (Children: 56% vs 48%; women: 50% vs 43%). Furthermore, stunted children and women with low BMI have significantly increased odds of developing anemia, as reflected by the adjusted ORs of 1.76 (95% CI:1.10-2.83) and 1.81 (95% CI: 1.11-3.48), respectively. The association of stunting with anemia in children was modified by their age and socio-economic condition, where risk of being anemic decreases with increasing age but with a lower rate for stunted children from richest family. In addition, stunted children of anemic mothers are at greater risk of being anemic compared to non-stunted children of anemic or non-anemic mothers. Again the association between BMI and anemia in women is modified by the level of education, with risk of anemia being lowest among women with low BMI and higher education. CONCLUSION: Evidence-based policies targeting the vulnerable groups are required to combat anemia and nutritional deficiencies simultaneously under the same program.

Determinants of frequency and contents of antenatal care visits in Bangladesh: Assessing the extent of compliance with the WHO recommendations.

BACKGROUND: In addition to the number of antenatal care (ANC) visits, the items of ANC services covered by ANC visits greatly influence the effectiveness of the ANC services. Recently the World Health Organization (WHO) recommended not only to achieve a minimum of eight ANC visits, but also to use a core set of items of ANC services for safe motherhood. This study examined the levels and determinants of frequency and contents of ANC visits in Bangladesh and thus assessed the level of compliance with the WHO recommended number and the content of ANC services during pregnancy in Bangladesh. METHODS: The data for the study come from the 2014 Bangladesh Demographic and Health Survey (BDHS), which covereda nationally representative sample of 17,863 ever-married women aged 15-49 years. Data derived from 4,627 mothers who gave birth in the three years preceding the survey constituted the study subjects. Descriptive, inferential and multivariate statistical techniques were used for data analysis. RESULTS: On average, mothers received less than three (2.7 visits) ANC visits and only 6% receive the recommended eight or more ANC visits. About 22% of the mothers received all the prescribed basic items of ANC services. About one-fifth (21%) of the mothers never received ANC visits and thus no items of ANC services. Measurement of blood pressure was the most common item received during ANC visit as reported by 69% mothers. Blood test was the least received item (43%). Significant positive association was found between frequency of ANC visits and receiving the increased number of items of ANC services. High socio-economic status, low parity, living in urban areas and certain administrative regions, planned pregnancies, having media exposure, visiting skilled providers for ANC services and visit to public or NGO health facilities are associated with frequent ANC visits and receiving higher number of items of ANC contents. CONCLUSION: An unsatisfactory level of coverage of and content of ANC visits have been observed in Bangladesh. Further investigation is needed to identify the causes of under-utilization of ANC services in Bangladesh. A greater understanding of the identified risk factors and incorporating them into short and long term strategies would help improve the coverage and contents and thus quality of ANC services in Bangladesh.

Health care seeking behaviour during pregnancy, delivery and the postnatal period in Bangladesh: Assessing the compliance with WHO recommendations.

BACKGROUND: The World Health Organization (WHO) recommends four antenatal care (ANC) visits, delivery in a health facility and three postnatal care (PNC) visits for women to optimize the maternal health outcomes. OBJECTIVES: To examine the level and determinants of maternal health care seeking behaviour during pregnancy, delivery and the postnatal period, and assess the compliance with the WHO recommended levels of care in Bangladesh. DESIGN/SETTING: The study is based on secondary analysis of the data obtained from the 2014 Bangladesh Demographic and Health Survey (BDHS). The 2014 BDHS was a cross-sectional survey of a nationally representative sample of 17,863 ever-married women aged 15-49 years. The sample was selected following a two-stage stratified cluster sampling design. PARTICIPANTS: The dataset from a subsample of 4.627 ever-married women who had delivered their last birth within three years before the survey were included in the analysis to meet the objectives of the study. ANALYSIS: Descriptive statistics and multinomial logistic regression model were used for data analysis. FINDINGS: It has been observed that only 31% mothers had recommended four or more ANC visits, 37% births were delivered at health facilities, and 65% mothers received at least one PNC visit. Only 18.0% mothers received the WHO recommended optimal level of four or more ANC visits, births in a health facility and at least one PNC visit. Mothers aged less than 20 years, living in rural area, having no education and media exposure, multiparous, poor wealth status, husband with no education and husband's employment status appeared as significant predictors of optimal level maternal health care after adjusting for other factors. Mothers living in Sylhet, Chittagong and Barisal regions were less likely to receive the optimum level health care. KEY CONCLUSION: Utilization of maternal health care during pregnancy, delivery and the postnatal period among Bangladeshi women does not reflect the complete compliance with the WHO recommendations. Further studies are needed to identify the reasons for underutilization of optimum level maternal care practice in Bangladesh. IMPLICATION FOR PRACTICE: The findings underscore the need for targeted intervention for those groups of mothers who were identified as having lowest level of maternal care across the continuum of care.

Modeling future water footprint of barley production in Alberta, Canada: Implications for water use and yields to 2064.

Despite the perception of being one of the most agriculturally productive regions globally, crop production in Alberta, a western province of Canada, is strongly dependent on highly variable climate and water resources. We developed agro-hydrological models to assess the water footprint (WF) of barley by simulating future crop yield (Y) and consumptive water use (CWU) within the agricultural region of Alberta. The Soil and Water Assessment Tool (SWAT) was used to develop rainfed and irrigated barley Y simulation models adapted to sixty-seven and eleven counties, respectively through extensive calibration, validation, sensitivity, and uncertainty analysis. Eighteen downscaled climate projections from nine General Circulation Models (GCMs) under the Representative Concentration Pathways 2.6 and 8.5 for the 2040-2064 period were incorporated into the calibrated SWAT model. Based on the ensemble of GCMs, rainfed barley yield is projected to increase while irrigated barley is projected to remain unchanged in Alberta. Results revealed a considerable decrease (maximum 60%) in WF to 2064 relative to the simulated baseline 1985-2009 WF. Less water will also be required to produce barley in northern Alberta (rainfed barley) than southern Alberta (irrigated barley) due to reduced water consumption. The modeled WF data adjusted for water stress conditions and found a remarkable change (increase/decrease) in the irrigated counties. Overall, the research framework and the locally adapted regional model results will facilitate the development of future water policies in support of better climate adaptation strategies by providing improved WF projections.