Assessment of Safe Drinking Water Handling Practices in Households of Northern India: A Cross-Sectional Study.

Background Waterborne diseases are the most common form of infectious disease, spreading from contaminated water, especially in a developed country. These diseases are a major concern for the environment and public health. The living conditions in developing countries like India affect the water-handling practices, which make the population vulnerable to waterborne diseases. The inability to access safe drinking water also adds to this. Water safety for a community relies on water collection, treatment, storage, and handling in the household setting. Therefore, the burden of waterborne disease can be reduced by treating point-of-use drinking water, including improving handling and transport. Objectives The aim was to assess the safe drinking water handling practices in households. The objectives were to assess the safe drinking water-handling practices, namely, treatment, storage, lid status of the storage vessel, and water drawing technique, and to estimate the sources of safe drinking water. Methods This cross-sectional study was conducted in the Etawah district on a total of 312 eldest female family members actively working in the kitchen. Descriptive analysis and Chi-Square test were applied to the collected data and a p-value <0.05 at 95% confidence interval (CI) was taken as statistically significant. Results Overall, 135 (85.9%) households in urban areas relied on public supply. However, in rural areas mostly 130 (83%) households depended on private supply. In water-handling practices, 276 (88.4%) used some method to purify drinking water, a total of 209 (67%) households kept the lid of the storage container covered, and 249 (79.8%) households drew water either by pouring or scooping with a long handle. Conclusion The study concluded that both private and public sources were used for drinking water. Regarding water-handling practices, most households drank purified water, kept their containers covered, and drew water either by scooping or pouring from storage containers. Those who drank purified water mostly belonged to nuclear families and had private sources of drinking water.

Estimation of Access to Safe Drinking Water to Households in Etawah District: A Cross-Sectional Study.

CONTEXT: Unsafe drinking water causes diarrheal disease and environmental enteropathy. The quality of water is determined by its physical, chemical, and biological characteristics. Water sources have a significant impact on household members' health, particularly children. To combat this, India is committed to providing household tap connections to ensure the delivery of safe drinking water with the "Jal Jeevan Mission." AIMS: This study aims to estimate the access to safe drinking water and the physical and chemical qualities of water (qualitatively) in the urban and rural areas of Etawah district, India. SETTINGS AND DESIGN: A cross-sectional study was conducted in Etawah district from January 2020 to December 2021. The study subjects were the eldest female of the family. A total of 312 females were included. The data collected were analyzed using IBM SPSS Statistics for Windows, version 25 (released 2017; IBM Corp., Armonk, New York, United States) for descriptive analysis. RESULTS: In the present study, 76.3% (238/312) of households in the urban and rural areas had access to safe drinking water (here, the meaning of the word "safe" is based on its operational definition). A total of 130 (83.3%) households in rural areas and only 21 (13.5%) in urban areas had private supply as the primary water source. The physical and chemical qualities of water were within the requirement (acceptable limit) and permissible limit in all the study areas, so the water is considered safe for drinking. CONCLUSIONS: This study reported that 76.3% (238) households had access to safe drinking water according to the operational definition. The major public source of drinking water was public-supplied tap water, and in private sources, submersible or boreholes were the most common.

Barriers of household toilet utilization among toilet owners in a rural area of Northern India: An analytical cross-sectional study.

Background: Open defecation continues to prevail among toilet owners despite effective implementation of the Swachh Bharat Mission (Gramin). We conducted this study to determine toilet utilization rates and learn about the barriers to toilet use in the rural areas. By understanding the barriers, physicians can provide targeted education and become better equipped to manage their patients' conditions and advocate for their demands. Materials and Methods: We conducted a cross-sectional study on the households of the rural field practice areas of the department in central Uttar Pradesh by the census method. House listing was procured from the departmental records. The questionnaire was directed at both the household level and individual level. Results: The proportion of households with access to a toilet was found to be 91.1% of which 504 households were included in the study. Among the toilet owners, 115 (22.8%) households were not using toilets exclusively by all the members. At the individual level, age groups (of 20-59 years, and ≥60 years) and female gender were found to be significantly associated with open defecation. At the household level, government assistance for toilet construction and livestock keeping was found to be associated with open defecation. Major barriers to toilet use were childhood habits, dearth of toilets in the farming grounds/workplace, women during menstruation and having a non-functional toilet. Conclusion: This study indicates that merely installing a household toilet does not ensure exclusive utilization of toilet and the practice of open defecation might continue to be prevalent if corrective measures are not undertaken.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm.

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.

Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using U-series architectures: An unseen Artificial Intelligence paradigm for stroke risk assessment.

Stroke risk assessment using deep learning (DL) requires automated, accurate, and real-time risk assessment while ensuring compact model size. Previous DL paradigms suffered from challenges like memory size, low speed, and complex in nature lacking multi-ethnic, and multi-institution databases. This research segments and measures the area of the plaque far wall of the common carotid (CCA) and internal carotid arteries (ICA) in B-mode ultrasound using four types of solo, namely, UNet, UNet+, UNet++, and UNet+++, and three types of hybrids, namely, Inception-UNet, Fractal-UNet, and Squeeze-UNet, architectures. These seven models are benchmarked against autoencoder-based solution. Three kinds of databases, namely, CCA, ICA, and combined CCA + ICA were implemented using K5 cross-validation protocol. This was validated using unseen Hong Kong data. The CCA database consisted of 379 Japanese images from low-to medium-risk, while the ICA database consisted of 970 Japanese images taken from 97 medium-to high-risk patients. Using the coefficient of correlation (CC) metric between automated measured area and manually delineated area, seven deep learning solo and hybrid models for CCA yielded 0.96, 0.96, 0.98, 0.95, 0.96, and 0.96 respectively, whereas ICA yielded 0.99, 0.99, 0.98, 0.99, 0.98, 0.98, and 0.98 respectively. Area under the receiver operating characteristics curve values for CCA images was 0.97, 0.969, 0.974, 0.969, 0.962, 0.969, and 0.960 respectively, whereas for ICA images were 0.99, 0.989, 0.988, 0.989, 0.986, 0.989, and 0.988, respectively (p < 0.001). The percentage improvement in offline memory size, training time and training parameters for Squeeze-UNet compared to UNet++ were 569%, 122.46%, and 569%, respectively.

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models.

Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes­including COVID-19­are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks­namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152­for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study.

BACKGROUND: The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture. METHODS: The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet. This model is benchmarked against UNet and advanced conventional models using scale-space such as AtheroEdge 2.0 (AtheroPoint, CA, USA). All our resultant statistics of the three systems were in the order of UNet, SegNet-UNet, and AtheroEdge 2.0. RESULTS: Using the database of 379 ultrasound scans from a Japanese cohort of 190 patients having moderate risk and implementing the cross-validation deep learning framework, our system performance using area-under-the-curve (AUC) for UNet, SegNet-UNet, and AtheroEdge 2.0 were 0.93, 0.94, and 0.95 (P<0.001), respectively. The coefficient of correlation between the three systems and ground truth (GT) were: 0.82, 0.89, and 0.85 (P<0.001 for all three), respectively. The mean absolute area error for the three systems against manual GT was 4.07±4.70 mm2, 3.11±3.92 mm2, 3.72±4.76 mm2, respectively, proving the superior performance SegNet-UNet against UNet and AtheroEdge 2.0, respectively. Statistical tests were also conducted for their reliability and stability. CONCLUSIONS: The proposed study demonstrates a fast, accurate, and reliable solution for early detection and quantification of plaque lesions in common carotid artery ultrasound scans. The system runs on a test US image in <1 second, proving overall performance to be clinically reliable.

Unseen Artificial Intelligence-Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study.

BACKGROUND: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the "same" ethnic group ("Seen AI"). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the "Unseen AI" paradigm where training and testing are from "different" ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between "Unseen AI" and "Seen AI". METHODOLOGY: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. "Unseen AI" (training: Japanese, testing: HK or vice versa) and "Seen AI" experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. RESULTS: When using the UNet DL architecture, the "Unseen AI" pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for "Unseen AI" pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using "Seen AI", the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. CONCLUSION: We demonstrated that "Unseen AI" was in close proximity (<10%) to "Seen AI", validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound.

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid artery (CCA). This study focuses on automated plaque segmentation in the internal carotid artery (ICA) using solo deep learning (SDL) and hybrid deep learning (HDL) models. The methodology consists of a novel design of 10 types of SDL/HDL models (AtheroEdge™ 3.0 systems (AtheroPoint™, Roseville, CA) with a depth of four layers each. Five of the models use cross-entropy (CE)-loss, and the other five models use Dice similarity coefficient (DSC)-loss functions derived from UNet, UNet+, SegNet, SegNet-UNet, and SegNet-UNet+. The K10 protocol (Train:Test:90%:10%) was applied for all 10 models for training and predicting (segmenting) the plaque region, which was then quantified to compute the plaque area in mm2. Further, the data augmentation effect was analyzed. The database consisted of 970 ICA B-mode US scans taken from 99 moderate to high-risk patients. Using the difference area threshold of 10 mm2 between ground truth (GT) and artificial intelligence (AI), the area under the curve (AUC) values were 0.91, 0.911, 0.908, 0.905, and 0.898, all with a p-value of <0.001 (for CE-loss models) and 0.883, 0.889, 0.905, 0.889, and 0.907, all with a p-value of <0.001 (for DSC-loss models). The correlations between the AI-based plaque area and GT plaque area were 0.98, 0.96, 0.97, 0.98, and 0.97, all with a p-value of <0.001 (for CE-loss models) and 0.98, 0.98, 0.97, 0.98, and 0.98 (for DSC-loss models). Overall, the online system performs plaque segmentation in less than 1 s. We validate our hypothesis that HDL and SDL models demonstrate comparable performance. SegNet-UNet was the best-performing hybrid architecture.

Impact of COVID-19 pandemic on maternal and child health services in Uttar Pradesh, India.

BACKGROUND: In the wake of the Covid-19 Pandemic, parts of the public health system at increased risk of reduced efficiency include healthcare services for women and children. This in turn could reverse all the progress achieved over the years in reducing maternal and child mortality. In this study, an attempt has been made to assess the indirect effect of the pandemic on maternal and child health services in public health facilities. METHODS: Data pertaining to maternal and child health services being provided under specific Government programmes, were collected from public health facilities of District Sant Kabir Nagar in Uttar Pradesh, India. Comparative analysis of the data from the pandemic phase with data from the year 2019 was done to determine the impact on services. RESULTS: Reduced coverage across all maternal and child health interventions was observed in the study. There was an overall decrease of 2.26 % in number of institutional deliveries. Antenatal care services were the worst affected with 22.91% decline. Immunization services were also dramatically decreased by more than 20%. CONCLUSION: The response of the public healthcare delivery system to the Covid-19 Pandemic is negatively affecting both the provision and utilization of maternal and child healthcare services. It is deterrent to the progress achieved in maternal and child health parameters over the years. Better response strategies should be put in place to minimize lag in service deliwvery.

Japanese encephalitis in Uttar Pradesh, India: A situational analysis.

INTRODUCTION: Japanese encephalitis (JE) is a vector-borne, viral illness caused by the Japanese Encephalitis Virus. Permanent neurologic or psychiatric sequelae can occur in 30%-50% of those with encephalitis; hence, JE is a cause of major public health concern. For the ease of diagnosis and facilitation of surveillance, National Vector Borne Disease Control Programme uses the term Acute Encephalitis Syndrome (AES). In this study, an attempt has been made to ascertain the status and trends of AES and JE in Uttar Pradesh, India. METHODOLOGY: This is a record-based retrospective study. The data were obtained from the Directorate of Medical and Health Services of Uttar Pradesh and analyzed using software SPSS version 24.0. RESULTS: In Uttar Pradesh, there were 47,509 reported cases of AES from 2005 to 2018,. With yearly fluctuations, the average Case Fatality Rate of AES was 17.49% with highest in 2005 (24.76%) and lowest in 2018 (8%). Among the patients with AES, 9.98% were found positive for JE. The most commonly affected age group is 1-5 years for both AES and JE, closely followed by the age group of 5-10 years. Peak occurrence of both AES and JE was recorded in month of September. Among the AES-affected patients 53.8% were males and 46.2% were females. CONCLUSION: The most commonly affected age group was 1-5 years with peak occurrence in the month of September. Though there was a downward trend in CFR, awareness activities like "Dastak" campaign and intersectoral preventive activities, needs to be strengthened.

Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach.

Cardiologists can acquire important information related to patients' cardiac health using carotid artery stiffness, its lumen diameter (LD), and its carotid intima-media thickness (cIMT). The sonographers primarily concern about the location of the artery in B-mode ultrasound images. Localization using manual methods is tedious and time-consuming and also may lead to some errors. On the other hand, automated approaches are more objective and can provide the localization of the artery at near real time. Above arterial parameters may be determined after localization of the artery in real time.A novel method of localization of common carotid artery (CCA) transverse section is presented in this work. The method is known as fast region convolutional neural network (FRCNN)-based localization method and is designed using a stack of three layers viz. convolutional layers, fully connected layers, and pooling layers. These organized layers constitute a region proposal network (RPN) and an object class detection network (OCDN). We obtain an outcome as a bounding box along with a score of prediction around the cross-section of the CCA.B-mode ultrasound image database of CCA is split into training and testing set, to accomplish this, three partition methods K = 2, 5, and 10 are used in our work. The training is extended for 30, 200, and 2000 epochs in order to achieve fine-tuned features from the convolutional neural network. After 2000 epochs, we obtain 95% validation accuracy; however, mean of the accuracies up to 2000 epochs is 89.36% for K = 10 partitions protocol (training 90%, testing 10%). Generated CNN model is tested on a different dataset of 433 images and the acquired accuracy is 87.99%. Thus, the proposed method including an advanced deep learning technique demonstrates promising localization for carotid artery transverse section. Graphical abstract.

Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm.

Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque. Further, this paper presents a scientific validation system for stroke risk assessment. Both these innovations have never been presented before. The methodology consists of an automated segmentation system of the near wall and far wall regions in grayscale carotid B-mode ultrasound scans. Sixteen grayscale texture features are computed, and fed into the machine learning system. The training system utilizes the lumen diameter to create ground truth labels for the stratification of stroke risk. The cross-validation procedure is adapted in order to obtain the machine learning testing classification accuracy through the use of three sets of partition protocols: (5, 10, and Jack Knife). The mean classification accuracy over all the sets of partition protocols for the automated system in the far and near walls is 95.08% and 93.47%, respectively. The corresponding accuracies for the manual system are 94.06% and 92.02%, respectively. The precision of merit of the automated machine learning system when compared against manual risk assessment system are 98.05% and 97.53% for the far and near walls, respectively. The ROC of the risk assessment system for the far and near walls is close to 1.0 demonstrating high accuracy.

PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

BACKGROUND AND OBJECTIVE: Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD: This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS: Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS: This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.