DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Effects of Commercial Probiotics on the Growth Performance, Intestinal Microbiota and Intestinal Histomorphology of Nile Tilapia (Oreochromis niloticus) Reared in Biofloc Technology (BFT)

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

Patient Satisfaction With the Health Care Services of a Government-Financed Health Protection Scheme in Bangladesh: Cross-Sectional Study

BACKGROUND

Since 2016, the government of Bangladesh has been piloting a health protection scheme known as Shasthyo Surokhsha Karmasuchi (SSK), which specifically targets households living below the poverty line. This noncontributory scheme provides enrolled households access to inpatient health care services for 78 disease groups. Understanding patients' experiences with health care utilization from the pilot SSK scheme is important for enhancing the quality of health care service delivery during the national-level scale-up of the scheme.

OBJECTIVE

We aimed to evaluate patient satisfaction with the health care services provided under the pilot health protection scheme in Bangladesh.

METHODS

A cross-sectional survey was conducted with the users of the SSK scheme from August to November 2019. Patients who had spent a minimum of 2 nights at health care facilities were selected for face-to-face exit interviews. During these interviews, we collected information on patients' socioeconomic characteristics, care-seeking experiences, and level of satisfaction with various aspects of health care service delivery. To measure satisfaction, we employed a 5-point Likert scale (very satisfied, 5; satisfied, 4; neither satisfied nor dissatisfied, 3; dissatisfied, 2; very dissatisfied, 1). Descriptive statistics, statistical inferential tests (t-test and 1-way ANOVA), and linear regression analyses were performed.

RESULTS

We found that 55.1% (241/438) of users were either very satisfied or satisfied with the health care services of the SSK scheme. The most satisfactory indicators were related to privacy maintained during diagnostic tests (mean 3.91, SD 0.64), physicians' behaviors (mean 3.86, SD 0.77), services provided at the registration booth (mean 3.86, SD 0.62), confidentiality maintained regarding diseases (mean 3.78, SD 0.72), and nurses' behaviors (mean 3.60, SD 0.83). Poor satisfaction was identified in the interaction of patients with providers about illness-related information (mean 2.14, SD 1.40), availability of drinking water (mean 1.46, SD 0.76), cleanliness of toilets (mean 2.85, SD 1.04), and cleanliness of the waiting room (mean 2.92, SD 1.09). Patient satisfaction significantly decreased by 0.20 points for registration times of 16-30 minutes and by 0.32 points for registration times of >30 minutes compared with registration times of ≤15 minutes. Similarly, patient satisfaction significantly decreased with an increase in the waiting time to obtain services. However, the satisfaction of users significantly increased if they received a complete course of medicines and all prescribed diagnostic services.

CONCLUSIONS

More than half of the users were satisfied with the services provided under the SSK scheme. However, there is scope for improving user satisfaction. To improve the satisfaction level, the SSK scheme implementation authorities should pay attention to reducing the registration time and waiting time to obtain services and improving the availability of drugs and prescribed diagnostic services. The authorities should also ensure the supply of drinking water and enhance the cleanliness of the facility.

A novel investigation of pressure-induced semiconducting to metallic transition of lead free novel Ba3SbI3 perovskite with exceptional optoelectronic properties

The structural, electronic, mechanical, and optical characteristics of barium-based halide perovskite Ba3SbI3 under the influence of pressures ranging from 0 to 10 GPa have been analyzed using first-principles calculations for the first time. The new perovskite Ba3SbI3 material was shown to be a direct band gap semiconductor at 0 GPa, but the band gap diminished when the applied pressure increased from 0 to 10 GPa. So the Ba3SbI3 material undergoes a transition from semiconductor to metallic due to high pressure at 10 GPa. The Ba3SbI3 material also exhibits an increase in optical absorption and conductivity with applied pressure due to the change in band gap, which is more suitable for solar absorbers, surgical instruments, and optoelectronic devices. The charge density maps confirm the presence of both ionic and covalent bonding characteristics. Exploration into the mechanical characteristics indicates that the Ba3SbI3 perovskite is mechanically stable. Additionally, the Ba3SbI3 compound becomes strongly anisotropic at high pressure. The insightful results of our simulations will all be helpful for the experimental structure of a new effective Ba3SbI3-based inorganic perovskite solar cell in the near future.

Idiotopes of antibodies against HIV-1 CD4-induced epitope shared with those against microorganisms

Induction of antibodies (Abs) against the conformational CD4-induced (CD4i) epitope is frequent in HIV-1 infection. However, the mechanism of development of anti-CD4i Abs is unclear. We used anti-idiotypic (aID) monoclonal Abs (mAbs) of anti-CD4i mAbs to isolate anti-CD4i mAbs from infected subjects and track the causative antigens. One anti-aID mAb sorted from infected subjects by aID mAbs had the characteristics of anti-CD4i Abs, including IGHV1-69 usage and ability to bind to HIV-1 Env enhanced by sCD4. Critical amino acid sequences for the binding of six anti-aID mAbs, with shared idiotope to anti-CD4i mAbs, were analysed by phage display. The identified amino acid sequences showed similarity to proteins from human microbiota and infectious agents. Peptides synthesized from Caudoviricetes sp and Vibrio vulnificus based on the identified sequences were reactive to most anti-aID and some anti-CD4i mAbs. These results suggest that anti-CD4i Abs may evolve from B cells primed by microorganisms.

The effectiveness of a government-sponsored health protection scheme in reducing financial risks for the below-poverty-line population in Bangladesh

The Government of Bangladesh is piloting a non-contributory health protection scheme called Shasthyo Surokhsha Karmasuchi (SSK) to increase access to quality essential healthcare services for the below-poverty-line (BPL) population. This paper assesses the effect of the SSK scheme on out-of-pocket expenditure (OOPE) for healthcare, catastrophic health expenditure (CHE) and economic impoverishment of the enrolled population. A comparative cross-sectional study was conducted in Tangail District, where the SSK was implemented. From August 2019 to March 2020, a total of 2315 BPL households (HHs) (1170 intervention and 1145 comparison) that had at least one individual with inpatient care experience in the last 12 months were surveyed. A household is said to have incurred CHE if their OOPE for healthcare exceeds the total (or non-food) HH's expenditure threshold. Multiple regression analysis was performed using OOPE, incidence of CHE and impoverishment as dependent variables and SSK membership status, actual BPL status and benefits use status as the main explanatory variables. Overall, the OOPE was significantly lower (P < 0.01) in the intervention areas (Bangladeshi Taka (BDT) 23 366) compared with the comparison areas (BDT 24 757). Regression analysis revealed that the OOPE, CHE incidence at threshold of 10% of total expenditure and 40% of non-food expenditure and impoverishment were 33% (P < 0.01), 46% (P < 0.01), 42% (P < 0.01) and 30% (P < 0.01) lower, respectively, in the intervention areas than in the comparison areas. Additionally, HHs that utilized SSK benefits experienced even lower OOPE by 92% (P < 0.01), CHE incidence at 10% and 40% threshold levels by 72% (P < 0.01) and 59% (P < 0.01), respectively, and impoverishment by 27% at 10% level of significance. These findings demonstrated the significant positive effect of the SSK in reducing financial burdens associated with healthcare utilization among the enrolled HHs. This illustrates the importance of the nationwide scaling up of the scheme in Bangladesh to reduce the undue financial risk of healthcare utilization for those in poverty.

Subclinical Measures of Cardiovascular Health Among Wildland Firefighters

OBJECTIVE

The aim of the study is to compare subclinical measures of cardiovascular health among wildland firefighters (WFFs) to the US general population.

METHODS

Our cross-sectional study compared body mass index, total cholesterol, and blood pressure in 11,051 WFFs aged 17 to 64 years using Department of the Interior Medical Screening Program clinical screening examinations between 2014-2018 to National Health and Nutrition Examination Survey of 2015-2016 cycle using adjusted logistic regression analyses.

RESULTS

The logistic regression model shows significantly higher odds of hypertension and prehypertension in WFFs (2.84 times more with 95% CI: 2.28-3.53) than US general population. There were no consistent differences in body mass index or total cholesterol between the two population.

CONCLUSIONS

Hypertension and prehypertension were more prevalent in WFFs compared with the US general population, which suggests the need for actions for protecting against cardiovascular disease among WFFs.

ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach

Anticancer Peptides (ACPs) offer significant potential as cancer treatment drugs in this modern era. Quickly identifying active compounds from protein sequences is crucial for healthcare and cancer treatment. In this paper ANNprob-ACPs, a novel and effective model for detecting ACPs has been implemented based on nine feature encoding techniques, including AAC, CC, W2V, DPC, PAAC, QSO, CTDC, CTDT, and CKSAAGP. After analyzing the performance of several machine learning models, the six best models were selected based on their overall performances in every evaluation metric. The probability scores of each model were subsequently aggregated and used as input of our meta- model, called ANNprob-ACPs. Our model outperformed all others and its potential to lead to phenomenal identification of ACPs. The results of this study showed notable improvement in 10-fold cross-validation and independent test, with accuracy of 93.72% and 90.62%, respectively. Our proposed model, ANNprob-ACPs outperformed existing approaches in terms of accuracy and effectiveness in discovering ACPs. By using SHAP, this study obtained the physicochemical properties of QSO, and compositional properties of DPC, AAC, and PAAC are more impactful for our model's performances, which have a major impact on a drug's interactions and future discoveries. Consequently, this model is crucial for the future and has a high probability of detecting ACPs more frequently. We developed a web server of ANNprob-ACPs, which is accessible at ANNprob-ACPs webserver.

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

PURPOSE

An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features.

METHOD

Ten informative features are extracted from the region of interest (ROI), based on the radiologists' diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model's performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities.

RESULTS

The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model's performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman's correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph.

CONCLUSION

The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.

Hidden magnetism uncovered in a charge ordered bilayer kagome material ScV6Sn6

Charge ordered kagome lattices have been demonstrated to be intriguing platforms for studying the intertwining of topology, correlation, and magnetism. The recently discovered charge ordered kagome material ScV6Sn6 does not feature a magnetic groundstate or excitations, thus it is often regarded as a conventional paramagnet. Here, using advanced muon-spin rotation spectroscopy, we uncover an unexpected hidden magnetism of the charge order. We observe an enhancement of the internal field width sensed by the muon ensemble, which takes place within the charge ordered state. More importantly, the muon spin relaxation rate below the charge ordering temperature is substantially enhanced by applying an external magnetic field. Taken together with the hidden magnetism found in AV3Sb5 (A = K, Rb, Cs) and FeGe kagome systems, our results suggest ubiqitous time-reversal symmetry-breaking in charge ordered kagome lattices.

Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.

Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model

Introduction

Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.

Purpose

The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study.

Method

Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images.

Result

The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset.

Conclusion

The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.

Non-contact optical spectroscopy for tumor-sensitive diffuse reflectance and fluorescence measurements on murine subcutaneous tissue models: Monte Carlo modeling and experimental validations

Fiber-optic probes are commonly used in biomedical optical spectroscopy platforms for light delivery and collection. At the same time, it was reported that the inconsistent probe-sample contact could induce significant distortions in measured optical signals, which consequently cause large analysis errors. To address this challenge, non-contact optical spectroscopy has been explored for tissue characterizations. However, existing non-contact optical spectroscopy platforms primarily focused on diffuse reflectance measurements and may still use a fiber probe in which the probe was imaged onto the tissue surface using a lens, which serves as a non-contact probe for the measurements. Here, we report a fiber-probe-free, dark-field-based, non-contact optical spectroscopy for both diffuse reflectance and fluorescence measurements on turbid medium and tissues. To optimize the system design, we developed a novel Monte Carlo method to simulate such a non-contact setup for both diffuse reflectance and fluorescence measurements on murine subcutaneous tissue models with a spherical tumor-like target. We performed Monte Carlo simulations to identify the most tumor-sensitive configurations, from which we found that both the depth of the light focal point in tissue and the lens numerical aperture would dramatically affect the system's tumor detection sensitivity. We then conducted tissue-mimicking phantom studies to solidify these findings. Our reported Monte Carlo technique can be a useful computational tool for designing non-contact optical spectroscopy systems. Our non-contact optical setup and experimental findings will potentially offer a new approach for sensitive optical monitoring of tumor physiology in biological models using a non-contact optical spectroscopy platform to advance cancer research.

Change in economic burden of diarrhoea in children under-five in Bangladesh: 2007 vs. 2018

Background

In Bangladesh, diarrhoea in children under-five is a major public health problem with cost implications. Although under-five diarrhoea mortality and morbidity have declined from 2007 to 2018, change in the economic burden is unknown. This study determined the change in the societal economic burden of under-five diarrhoea in Bangladesh comparing 2007 to 2018.

Methods

A prevalence-based, retrospective cost analysis was conducted from a societal perspective, including costs to households, providers, and economic loss from premature deaths. Data were obtained from the previous cost of illness studies, government reports, and international databases. Direct costs for treatment were estimated by the bottom-up costing approach. Indirect costs on the loss of productivity of caretakers and loss from premature deaths were calculated by the human capital method. Total costs were presented in both local currency (Bangladeshi Taka (BDT)) and US dollars (US$)) in 2018 price. Sensitivity analyses were conducted to assess the robustness of the input parameters.

Results

A 36.4% reduction was found on the economic burden of under-five diarrhoea when comparing 2007 and 2018; US$1 209 million (95% CI = 1066 million-1299 million) for 2007 and US$769 million (95% CI = 484 million-873 million) for 2018. Economic loss from premature deaths imposed the highest costs (2007 = 66%, 2018 = 66% of all) followed by indirect costs on the loss of productivity of caretakers (2007 = 21%, 2018 = 26%) and direct medical costs (2007 = 13%, 2018 = 8%).

Conclusions

Societal costs from diarrhoeal diseases were reduced from 2007 to 2018 in Bangladesh. However, the economic burden was equivalent to 0.29% of country's gross domestic product in 2018 and remains a challenge. The major contributor to the costs was premature mortality from diarrhoeal diseases. Premature deaths are still prevalent though they to a large extent are avoidable. To further limit the economic burden, under-five diarrhoea mortality and morbidity reduction should be accelerated.

Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters on in vitro cancer cells and organotypic fresh tumor slices

To enable non-destructive metabolic characterizations on in vitro cancer cells and organotypic tumor models for therapeutic studies in an easy-to-access way, we report a highly portable optical spectroscopic assay for simultaneous measurement of glucose uptake and mitochondrial function on various cancer models with high sensitivity. Well-established breast cancer cell lines (MCF-7 and MDA-MB-231) were used to validate the optical spectroscopic assay for metabolic characterizations, while fresh tumor samples harvested from both animals and human cancer patients were used to test the feasibility of our optical metabolic assay for non-destructive measurement of key metabolic parameters on organotypic tumor slices. Our optical metabolic assay captured that MCF-7 cells had higher mitochondrial metabolism, but lower glucose uptake compared to the MDA-MB-231 cells, which is consistent with our microscopy imaging and flow cytometry data, as well as the published Seahorse Assay data. Moreover, we demonstrated that our optical assay could non-destructively measure both glucose uptake and mitochondrial metabolism on the same cancer cell samples at one time, which remains challenging by existing metabolic tools. Our pilot tests on thin fresh tumor slices showed that our optical assay captured increased metabolic activities in tumors compared to normal tissues. Our non-destructive optical metabolic assay provides a cost-effective way for future longitudinal therapeutic studies using patient-derived organotypic fresh tumor slices through the lens of tumor energetics, which will significantly advance translational cancer research.

Low-cost compact optical spectroscopy and novel spectroscopic algorithm for point-of-care real-time monitoring of nanoparticle delivery in biological tissue models

OBJECTIVE

Real-time monitoring of nanoparticle delivery in biological models is essential to optimize nanoparticle-mediated therapies. However, few techniques are available for convenient real-time monitoring of nanoparticle concentrations in tissue samples. This work reported novel optical spectroscopic approaches for low-cost point-of-care real-time quantification of nanoparticle concentrations in biological tissue samples.

METHODS

Fiber probe measured diffuse reflectance can be described with a simple analytical model by introducing an explicit dependence on the reduced scattering coefficient. Relying on this, the changes on the inverse of diffuse reflectance are proportional to absorption change when the scattering perturbation is negligible. We developed this model with proper wavelength pairs and implemented it with both a standard optical spectroscopy platform and a low-cost compact spectroscopy device for near real-time quantification of nanoparticle concentrations in biological tissue models.

RESULTS

Both tissue-mimicking phantom and ex vivo tissue sample studies showed that our optical spectroscopic techniques could quantify nanoparticle concentrations in near real-time with high accuracies (less than 5% error) using only a pair of narrow wavelengths (530 nm and 630 nm).

CONCLUSION

Novel low-cost point-of-care optical spectroscopic techniques were demonstrated for rapid accurate quantification of nanoparticle concentrations in tissue-mimicking medium and ex vivo tissue samples using optical signals measured at a pair of narrow wavelengths.

SIGNIFICANCE

Our methods will potentially facilitate real-time monitoring of nanoparticle delivery in biological models using low-cost point-of-care optical spectroscopy platforms, which will significantly advance nanomedicine in cancer research.

Occurrence of shigellosis in pediatric diarrheal patients in Chattogram, Bangladesh: A molecular based approach

Shigellaa Gram-negative, non-motile bacillus, is the primary causative agent of the infectious disease shigellosis, which kills 1.1 million people worldwideevery year. The children under the age of five are primarily the victims of this disease. This study has been conducted to assess the prevalence of shigellosis through selective plating, biochemical test and conventional PCR assays, where the samples were collected from suspected diarrheoal patients. Invasive plasmid antigen H (ipaH) and O-antigenic rfc gene were used to identify Shigella spp. and S. flexneri respectively. For validation of these identification, PCR product of ipaH gene of a sample (Shigella flexneri MZS 191) has been sequenced and submitted to NCBI database (GenBank accession no- MW774908.1). Further this strain has been used as positive control. Out of 204, around 14.2% (n = 29)(P> 0.01) pediatric diarrheoal cases were screened as shigellosis. Another interesting finding was that most of shigellosis affected children were 7 months to 1 year (P> 0.01).The significance of this study lies in the analyses of the occurrenceand the molecular identification of Shigellaspp. and S. flexneri that can be utilized in improving the accurate identification and the treatment of the most severe and alarming shigellosis.

Costs of services and funding gap of the Bangladesh National Tuberculosis Control Programme 2016-2022: An ingredient based approach

BACKGROUND

Bangladesh National Tuberculosis (TB) Control Programme (NTP) has deployed improved diagnostic technologies which may drive up the programme costs. We aimed to estimate the supply-side costs associated with the delivery of the NTP and the funding gap between the cost of implementation and available funding for the Bangladesh NTP.

METHODS

An ingredient-based costing approach was applied using WHO's OneHealth Tool software. We considered 2016, as the base year and projected cost estimates up to 2022 using information on NTP planned activities. Data were collected through consultative meetings with experts and officials/managers, review of documents and databases, and visits to five purposively selected TB healthcare facilities. The estimated costs were compared with the funds allocated to the NTP between 2018 and 2022 to estimate the funding gap.

FINDINGS

The estimated total cost of NTP was US$ 49.22 million in 2016, which would increase to US$ 146.93 million in 2022. Human resources (41.1%) and medicines and investigations/ supplies (38.0%) were the major two cost components. Unit costs were highest for treating extensively drug-resistant TB at US$ 7,422.4 in 2016. Between 2018-2022, NTP would incur US$ 536.8 million, which is US$ 235.18 million higher than the current allocation for NTP.

CONCLUSION

Our results indicated a funding gap associated with the NTP in each of the years between 2018-2022. Policy planners should advocate for additional funding to ensure smooth delivery of TB services in the upcoming years. The cost estimates of TB services can also be used for planning and budgeting for delivering TB services in similar country contexts.

The Prevalence of Multi-Drug Resistant Salmonella typhi Isolated From Blood Sample

Typhoid is a major public health concern. Even though antibiotics are usually used to treat typhoid fever, the spread of multi drug resistant Salmonella typhi is making antibiotics much less effective. This study was conducted to assess the prevalence of multidrug-resistant Salmonella typhi from the clinical samples. During this study, 154 blood samples of suspected typhoid patients were collected from the hospital and diagnostic center located in Chattogram City, Bangladesh. Isolation and identification of Salmonella typhi was done by both biochemical tests. PCR analysis was also done for the confirmation of biochemical result. Antimicrobial susceptibility test was performed according to the Kirby-Bauer disk diffusion method against ampicillin, chloramphenicol, cefepime, cotrimoxazole, ceptriaxone, ciprofloxacin, nalidixic acid, and azithtomycin. Out of 154, 21 (13.64%) isolates were identified as Salmonella typhi and the prevalence of typhoid in Chattogram, Bangladesh was 13.64% (n = 21). It was also found that children under the age of 5 are the more vulnerable target of Salmonella typhi infection. Antibiotic resistance profiling revealed 85% isolates were Multi-Drug Resistant (MDR) and highest resistance was found in case of Nalidixic acid. Although, most of the isolated Salmonella typhi were MDR, first generation antibiotics Co-trimoxazile, Chloramphenicol, and Ampicillin were found effective against Salmonella typhi.

Serum Magnesium Level and It's Relation in Predicting Adverse In-Hospital Outcome in Patients with First Attack of Myocardial Infarction

Acute myocardial infarction (AMI) patients characterize a large proportion of admissions in coronary care unit and their management and risk stratification is of immense importance. Hypomagnesemia is a long-term risk factor for incident of both myocardial infarction and arrhythmia. We assessed whether serum magnesium levels at admission is associated with arrhythmias and in-hospital mortality in patients with acute myocardial infarction (AMI). The aim of the study was to evaluate the prognostic implications of serum magnesium level in patients with acute myocardial infarction. This cross-sectional observational study was conducted in the department of cardiology in Mymensingh Medical College Hospital from October 2017 to March 2019. Total 259 acute myocardial infarction patients were included considering inclusion and exclusion criteria. The sample population was divided into two groups: Group-I: Patients with acute myocardial infarction with serum magnesium ≥1.82mg/dl. Group-II: Patients with acute myocardial infarction with serum magnesium <1.82mg/dl. Serum magnesium level was measured on admission, and the incidence of in-hospital major cardiac events was assessed. In this study mean serum magnesium level of Group-I, Group-II were 2.21±0.14mg/dl, 1.60±0.15mg/dl respectively. It was statistically significant (p<0.05). In-hospital outcomes of the study group revealed that low risk group patients were uneventful outcome during hospitalization period, they had no any complication. In Group-I patient, 9(4.8%) were developed arrhythmias, 26(13.9%) were developed heart failure, 9(4.8%) were developed cardiogenic shock and 3(1.6%) were died and in Group-II patient, 44(61.10%) developed arrhythmias, 9(12.50%) were developed heart failure, 7(9.7%) were developed cardiogenic shock and 12(16.7%) were died out of them which was statistically significant (p<0.05). Mean duration of hospital stay of the study population according serum magnesium level was in Group-I, 4.27±0.68 days, in Group-II, 5.84±1.05 days which was statistically significant (p<0.05). In conclusion patient with serum magnesium level less than 1.82mg/dl increased the risk of in-hospital arrhythmia and death.

Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.

Changes in Serum Uric Acid Level in Acute Myocardial Infarction Patients

Myocardial infarction (MI) is one of the dangerous manifestations of coronary artery disease and one of the commonest causes of mortality. This cross-sectional study was carried out in the department of Biochemistry, Mymensingh Medical College in collaboration with the department of Cardiology, Mymensingh Medical College Hospital, Mymensingh, Bangladesh during the period of January 2018 to December 2018. A total of 120 subjects were included in this study. Among them 60 were diagnosed AMI patients denoted as case group and 60 were apparently normal healthy individuals denoted as control group. Biochemical values were expressed as Mean±SD (Standard deviation). Statistical analysis was done by using SPSS (Statistical package for social science) version 21.0 windows package. Serum uric acid determined by enzymatic colorimetric method using the test kit. Among the study groups the mean±SD values of uric acid were 6.61±2.62 and 5.38±1.16mg/dl in case and control group respectively. The analysis showed that, serum uric acid was statistically increased in case group compared with control group. The level of significance was 0.001. Statistical significance of difference between two groups were evaluated by using Student's unpaired 't' test.

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

Knowledge, attitude, and practice of Bangladeshi urban slum dwellers towards COVID-19 transmission-prevention: A cross-sectional study

The first COVID-19 case in Bangladesh was detected on March 8, 2020. Since then, efforts are being made across the country to raise awareness among the population for preventing the spread of this virus. We aimed to examine the urban slum dwellers' knowledge, attitude, and practice (KAP) towards COVID-19 transmission-prevention. A phone-based cross-sectional survey was conducted in five slums of Dhaka City. Total 476 adult slum dwellers were interviewed between October 31 to December 1, 2020 using a pre-tested questionnaire. During an interview, information was collected on participants' demographic characteristics and KAP items towards COVID-19. We used quartiles for categorization of knowledge and practice score where the first quartile represents poor, the second and third quartiles represent average while the fourth quartile represents good. Attitude score was standardized using z-score and identified as positive and negative attitude. Multiple linear regression models were used separately to identify the socioeconomic predictors of the KAP scores. The results showed that 25% of the respondents had good knowledge and 25% had poor knowledge, 48% had a positive attitude and 52% had a negative attitude, and 21% maintained good practice and 33% maintained poor practice towards COVID-19 transmission-prevention. About 75% respondents relied on television for COVID-19 related information. Regression results showed that knowledge and attitude scores were significantly higher if respondents had primary or secondary and above level of education compared to the uneducated group. Female respondents maintained significantly good practice compared to their male counterparts (β = 6.841; p<0.01). This study has found that one third of the studied slum dwellers maintained poor practice and one fourth had poor knowledge towards COVID-19 transmission-prevention. As KAP domains are significantly correlated, efforts are needed to raise awareness of COVID-19 particularly targeting individuals with average and lower knowledge to improve attitude and practice for the prevention of COVID-19.

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

Factors affecting the healthcare utilization from Shasthyo Suroksha Karmasuchi scheme among the below-poverty-line population in one subdistrict in Bangladesh: a cross sectional study

BACKGROUND

Financing healthcare through out-of-pocket (OOP) payment is a major barrier in accessing healthcare for the poor people. The Health Economics Unit (HEU) of the Ministry of Health and Family Welfare of the government of Bangladesh has developed Shasthyo Suroksha Karmasuchi (SSK), a health protection scheme, with the aim of reducing OOP expenditure and improving access of the below-poverty-line (BPL) population to healthcare. The scheme started piloting in 2016 at Kalihati sub-district of Tangail District. Our objective was to assess healthcare utilization by the enrolled BPL population and to identify the factors those influencing their utilization of the scheme.

METHOD

A cross-sectional household survey was conducted from July to September 2018 in the piloting sub-district. A total of 806 households were surveyed using a semi-structured questionnaire. Information on illness and sources of healthcare service were captured for the last 90 days before the survey. Multiple logistic regression models were applied to determine the factors related to utilization of healthcare from the SSK scheme and other medically trained providers (MTPs) by the SSK members for both inpatient and outpatient care.

RESULT

A total of 781 (24.6%) people reported of suffering from illness of which 639 (81.8%) sought healthcare from any sources. About 8.0% (51 out of 639) of them sought healthcare from SSK scheme and 28.2% from other MTPs within 90 days preceding the survey. Households with knowledge about SSK scheme were more likely to utilize healthcare from the scheme and less likely to utilize healthcare from other MTPs. Non-BPL status and suffering from an accident/injury were significantly positively associated with utilization of healthcare from SSK scheme.

CONCLUSION

Among the BPL population, healthcare utilization from the SSK scheme was very low compared to that of other MTPs. Effective strategies should be in place for improving knowledge of BPL population on SSK scheme and the benefits package of the scheme should be updated as per the need of the target population. Such initiative can be instrumental in increasing utilization of the scheme and ultimately will reduce the barriers of OOP payment among BPL population for accessing healthcare.

Health system impact of COVID-19 on urban slum population of Bangladesh: a mixed-method rapid assessment study

OBJECTIVE

We aimed to rapidly assess the health system impact of COVID-19 in the urban slums of Bangladesh.

DESIGN

Setting and participantsA cross-sectional survey among 476 households was conducted during October-December 2020 in five selected urban slums of Dhaka North, Dhaka South and Gazipur City Corporation. In-depth interviews with purposively selected 22 slum dwellers and key informant interviews with 16 local healthcare providers and four policymakers and technical experts were also conducted.

OUTCOME MEASURES

Percentage of people suffering from general illness, percentage of people suffering from chronic illness, percentage of people seeking healthcare, percentage of people seeking maternal care, health system challenges resulting from COVID-19.

RESULTS

About 12% of members suffered from general illness and 25% reported chronic illness. Over 80% sought healthcare and the majority sought care from informal healthcare providers. 39% of the recently delivered women sought healthcare in 3 months preceding the survey. An overall reduction in healthcare use was reported during the lockdown period compared with prepandemic time. Mismanagement and inefficient use of resources were reported as challenges of health financing during the pandemic. Health information sharing was inadequate at the urban slums, resulting from the lack of community and stakeholder engagement (51% received COVID-19-related information, 49% of respondents knew about the national hotline number for COVID-19 treatment). Shortage of human resources for health was reported to be acute during the pandemic, resulting from the shortage of specialist doctors and uneven distribution of health workforce. COVID-19 test was inadequate due to the lack of adequate test facilities and stigma associated with COVID-19. Lack of strong leadership and stakeholder engagement was seen as the barriers to effective pandemic management.

CONCLUSION

The findings of the current study are expected to support the government in tailoring interventions and allocating resources more efficiently and timely during a pandemic.

Perovskite-structure TlBO3 (B = Cr, Mn) for thermomechanical and optoelectronic applications: an investigation via a DFT scheme

Here, we have employed the density functional theory on TlBO3 (B = Cr, Mn) to study the structural, mechanical, electronic, optical, and thermal properties for the first time. Spin polarization causes a metallic-to-semiconducting transition.

Photocurrent-driven transient symmetry breaking in the Weyl semimetal TaAs

Symmetry plays a central role in conventional and topological phases of matter, making the ability to optically drive symmetry changes a critical step in developing future technologies that rely on such control. Topological materials, like topological semimetals, are particularly sensitive to a breaking or restoring of time-reversal and crystalline symmetries, which affect both bulk and surface electronic states. While previous studies have focused on controlling symmetry via coupling to the crystal lattice, we demonstrate here an all-electronic mechanism based on photocurrent generation. Using second harmonic generation spectroscopy as a sensitive probe of symmetry changes, we observe an ultrafast breaking of time-reversal and spatial symmetries following femtosecond optical excitation in the prototypical type-I Weyl semimetal TaAs. Our results show that optically driven photocurrents can be tailored to explicitly break electronic symmetry in a generic fashion, opening up the possibility of driving phase transitions between symmetry-protected states on ultrafast timescales.

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Simple Summary

Breast cancer diagnosis at an early stage using mammography is important, as it assists clinical specialists in treatment planning to increase survival rates. The aim of this study is to construct an effective method to classify breast images into four classes with a low error rate. Initially, unwanted regions of mammograms are removed, the quality is enhanced, and the cancerous lesions are highlighted with different artifacts removal, noise reduction, and enhancement techniques. The number of mammograms is increased using seven augmentation techniques to deal with over-fitting and under-fitting problems. Afterwards, six fine-tuned convolution neural networks (CNNs), originally developed for other purposes, are evaluated, and VGG16 yielded the highest performance. We propose a BreastNet18 model based on the fine-tuned VGG16, changing different hyper parameters and layer structures after experimentation with our dataset. Performing an ablation study on the proposed model and selecting suitable parameter values for preprocessing algorithms increases the accuracy of our model to 98.02%, outperforming some existing state-of-the-art approaches. To analyze the performance, several performance metrics are generated and evaluated for every model and for BreastNet18. Results suggest that accuracy improvement can be obtained through image pre-processing techniques, augmentation, and ablation study. To investigate possible overfitting issues, a k-fold cross validation is carried out. To assert the robustness of the network, the model is tested on a dataset containing noisy mammograms. This may help medical specialists in efficient and accurate diagnosis and early treatment planning.

Abstract

Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.

COVID-19 Knowledge, Attitudes, and Practices Among People in Bangladesh: Telephone-Based Cross-sectional Survey

Background

The world has been grappling with the COVID-19 pandemic, a dire public health crisis, since December 2019. Preventive and control measures have been adopted to reduce the spread of COVID-19. To date, the public’s knowledge, attitudes, and practices regarding COVID-19 across Bangladesh have been poorly understood. Therefore, it is important to assess people’s knowledge, attitudes, and practices (KAP) toward the disease and suggest appropriate strategies to combat COVID-19 effectively.

Objective

This study aimed to assess the KAP of Bangladeshi people toward COVID-19 and to identify their determinants.

Methods

We conducted a country-wide cross-sectional telephonic survey from May 7 to 29, 2020. A purposive sampling method was applied, and adult Bangladeshi citizens who have mobile phones were approached to participate in the survey. Interviews were conducted based on verbal consent. Multiple logistic regression analyses and several tests were performed to identify the factors associated with KAP related to COVID-19.

Results

A total of 492 of 576 Bangladeshi adults aged 18 years and above completed the interview, with a response rate of 85.4% (492/576). Of the 492 participants, 321 (65.2%) were male, and 304 (61.8%) lived in a rural area. Mean scores for knowledge, attitudes, and practices were 10.56 (SD 2.86), 1.24 (SD 0.83), and 3.17 (SD 1.5), respectively. Among the 492 respondents, 273 (55.5%) had poor knowledge, and 251 (49%) expressed a negative attitude; 192 out of 359 respondents (53.5%) had poor practices toward COVID-19. Mean scores of knowledge, attitudes, and practices differed significantly across various demographic and socioeconomic groups. Rural residents had lower mean scores of knowledge (mean 9.8, SD 3.1, P<.001) and adherence to appropriate practice measures (mean 4, SD 1.4, P<.001) compared to their urban counterparts. Positive and statistically strong correlations between knowledge and attitudes (r=0.21, P<.001), knowledge and practices (r=0.45, P<.001), and attitudes and practices (r=0.27, P<.001) were observed. Television (53.7%) was identified as the major source of knowledge regarding COVID-19. Almost three-quarters of the respondents (359/492, 73%) went outside the home during the lockdown period. Furthermore, the study found that good knowledge (odds ratio [OR] 3.13, 95% CI 2.03-4.83, and adjusted OR 2.33, 95% CI 1.16-4.68) and a positive attitude (OR 2.43, 95% CI 1.59-3.72, and adjusted OR 3.87, 95% CI 1.95-7.68) are significantly associated with better practice of COVID-19 health measures.

Conclusions

Evidence-informed and context-specific risk communication and community engagement, and a social and behavior change communication strategy against COVID-19 should be developed in Bangladesh based on the findings of this study, targeting different socioeconomic groups.

Effectiveness of health voucher scheme and micro-health insurance scheme to support the poor and extreme poor in selected urban areas of Bangladesh: An assessment using a mixed-method approach

Background

National healthcare financing strategy recommends tax-based equity funds and insurance schemes for the poor and extreme poor living in urban slums and pavements as the majority of these population utilise informal providers resulting in adverse health effects and financial hardship. We assessed the effect of a health voucher scheme (HVS) and micro-health insurance (MHI) scheme on healthcare utilisation and out-of-pocket (OOP) payments and the cost of implementing such schemes.

Methods

HVS and MHI schemes were implemented by Concern Worldwide through selected NGO health centres, referral hospitals, and private healthcare facilities in three City Corporations of Bangladesh from December 2016 to March 2020. A household survey with 1,294 enrolees, key-informant interviews, focus group discussions, consultative meetings, and document reviews were conducted for extracting data on healthcare utilisation, OOP payments, views of enrolees, and suggestions of implementers, and costs of services at the point of care.

Results

Healthcare utilisation including maternal, neonatal and child health (MNCH) services, particularly from medically trained providers, was higher and OOP payments were lower among the scheme enrolees compared to corresponding population groups in general. The beneficiaries were happy with their access to healthcare, especially for MNCH services, and their perceived quality of care was fair enough. They, however, suggested expanding the benefits package, supported by an additional workforce. The cost per beneficiary household for providing services per year was €32 in HVS and €15 in MHI scheme.

Conclusion

HVS and MHI schemes enabled higher healthcare utilisation at lower OOP payments among the enrolees, who were happy with their access to healthcare, particularly for MNCH services. However, they suggested a larger benefits package in future. The provider’s costs of the schemes were reasonable; however, there are potentials of cost containment by purchasing the health services for their beneficiaries in a competitive basis from the market. Scaling up such schemes addressing the drawback would contribute to achieving universal health coverage.

Abstract 1925: Effect of Averrhoa carambola extract on the antineoplastic and apoptotic activities in mice

Medicinal plants play significant roles in the treatment of different diseases. Averrhoa carambola has important clinical values in the treatment of cancer. However, its antineoplastic and apoptotic activities are still obscure. In this study, we were aimed to investigate those potentials of Averrhoa carambola in mice. For this, first the powder, prepared from the leaf and flower of Averrhoa carambola, were suspended with methanol to yield methanol extract of leaf and flower. Antineoplastic effect of leaf (25 and 50 mg/kg) against Ehrlich Ascites Carcinoma (EAC) was assessed by evaluating the viable tumor cell count, survival time, body weight and hematological parameters of EAC cell bearing mice. The result showed that the treatment with leaf significantly (p&lt;0.05) decreased viable EAC cells and body weight gain thereby increasing the life span of EAC bearing mice and also revive the altered hematological (Hb, total RBC and WBC) parameters compare to those with normal values. Fluorescence microscopic view of 4,6-diamino-2-phenylindole (DAPI) staining demonstrated that leaf induced the alterations (i.e., nuclear condensation, fragmentation, membrane blabbing and apoptosis of cells) in the morphology of EAC cells. And, the quantitative analysis of p53 mRNA level by RT-PCR showed that p53 gene expression in leaf treated EAC cell was upregulated in respect to untreated control. These findings indicate that the induction of apoptosis in leaf-treated EAC cell was probably mediated through p53-dependent pathway. From this study, it can be concluded that the leaf of Averrhoa carambola exhibited potent antineoplastic and apoptotic activities that might be a potential agent for prevention of cancer in mice.

Citation Format: Md. Zahid Hasan, Ayesha Siddika. Effect of Averrhoa carambola extract on the antineoplastic and apoptotic activities in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1925.

The economic burden of diarrhea in children under 5 years in Bangladesh

BACKGROUND

Diarrhea is a leading cause of morbidity and mortality among under-five children in Bangladesh. Hospitalization for diarrhea can pose a significant burden on households and health systems. The aim of this study was to estimate the cost of illness due to diarrhea from the healthcare facility, caregiver, and societal perspectives in Bangladesh.

METHOD

A cross-sectional study with an ingredient-based costing approach was conducted in 48 healthcare facilities in Bangladesh. In total, 899 caregivers of under-five children with diarrhea were interviewed face-to-face between August 2017 and May 2018, followed up over phone after 7-14 days of discharge, to capture all expenses and time costs related to the entire episode of diarrhea.

RESULTS

The average cost per episode for caregivers was US$62, with $29 direct and $34 indirect costs. From the societal perspective, average cost per episode of diarrhea was $71. In 2018, an estimated $79 million of economic costs were incurred for treating diarrhea in Bangladesh. Using 10% of income as threshold, over 46% of interviewed households faced catastrophic expenditure from diarrheal disease.

CONCLUSION

The economic costs incurred by caregivers for treating per-episode of diarrhea was around 4% of the annual national gross domestic product per-capita. Investment in vaccination can help to reduce the prevalence of diarrheal diseases and avert this public health burden.

Assessing the incidence of catastrophic health expenditure and impoverishment from out-of-pocket payments and their determinants in Bangladesh: evidence from the nationwide Household Income and Expenditure Survey 2016

Background

Out-of-pocket (OOP) payments for healthcare have been increasing steadily in Bangladesh, which deteriorates the financial risk protection of many households.

Methods

We aimed to investigate the incidence of catastrophic health expenditure (CHE) and impoverishment from OOP payments and their determinants. We employed nationally representative Household Income and Expenditure Survey 2016 data with a sample of 46 076 households. A household that made OOP payments of >10% of its total or 40% of its non-food expenditure was considered to be facing CHE. We estimated the impoverishment using both national and international poverty lines. Multiple logistic models were employed to identify the determinants of CHE and impoverishment.

Results

The incidence of CHE was estimated as 24.6% and 10.9% using 10% of the total and 40% of non-food expenditure as thresholds, respectively, and these were concentrated among the poor. About 4.5% of the population (8.61 million) fell into poverty during 2016. Utilization of private facilities, the presence of older people, chronic illness and geographical location were the main determinants of both CHE and impoverishment.

Conclusion

The financial hardship due to OOP payments was high and it should be reduced by regulating the private health sector and covering the care of older people and chronic illness by prepayment-financing mechanisms.

Pharmacological insights on the antidepressant, anxiolytic and aphrodisiac potentials of Aglaonema hookerianum Schott

ETHNOPHARMACOLOGICAL RELEVANCE

Aglaonema hookerianum Schott is an ethnomedicinally important plant used to treat a variety of diseases, including sexual and depression-like disorders. However, the scientific basis underlying the aforesaid properties have not been well justified.

AIM OF THE STUDY

The present investigation aimed to investigate the anxiolytic, antidepressant and aphrodisiac potentials of methanol leaves extract of A. hookerianum (MEAH) in Swiss albino mice.

MATERIALS & METHODS

Swiss albino mice (20-30 g) were orally administrated with MEAH at the doses ranging from 100 to 400 mg/kg, b.w. The elevated plus maze (EPM) and hole board test (HBT) were performed to determine the anxiolytic activity and the forced swimming test (FST) and tail suspension test (TST) were performed to determine the antidepressant activity of MEAH. Besides, the aphrodisiac activity of MEAH was conducted through the mounting behaviour and orientation behaviour analysis. Diazepam (1 mg/kg, b.w., i.p.) for EPM and HBT; fluoxetine HCl (20 mg/kg, b.w., p.o.) for FST and TST, and sildenafil (5 mg/kg, b.w., p.o.) for the mounting behaviour analysis and orientation behaviour analysis were used as reference drugs.

RESULTS

The administration of the MEAH produced a strong (p < 0.001) dose-dependent anxiolytic effects in both HBT and EPM tests. Likewise, the extract revealed a significant (p < 0.001) reduction in the immobility time in both FST and TST as compared to the control group. Besides, the MEAH also found to possess marked aphrodisiac activity complying several facets such as an increase in the sexual performance at the highest dose (400 mg/kg, p.o.) as well as the orientation toward female mice (p < 0.001) at all tested doses.

CONCLUSION

Taken together, MEAH can be recommended as a potent source of neuroprotective and a libido-boosting drug candidate for the management of neurological and sexual disorders.

Amnion and collagen-based blended hydrogel improves burn healing efficacy on a rat skin wound model in the presence of wound dressing biomembrane

BACKGROUND

A burn wound is one of the most frequent and devastating injuries for patients which requires extensive care. Early treatment of burn wounds improves healing significantly.

OBJECTIVE

This study was designed to investigate the efficacy of amnion and collagen-based hydrogels on cutaneous burn wound healing in rats with covering membrane.

METHODS

We prepared a novel cell free hydrogel comprising human amnion, rabbit collagen, carboxymethyl cellulose sodium salt, citric acid, methyl paraben, propyl paraben, glycerin and triethanol amine. The wound covering membrane was developed from rabbit collagen and prawn shell chitosan. Beside swelling ratio, water absorption, equilibrium water content, gel fraction and spreadability analysis, in vitro cytotoxicity and biocompatibility tests were performed for the formulated hydrogels. Following the skin irritation study, second-degree burns were created on the dorsal region of the rats and the gels were applied with/without covering membrane to study the wound contraction and re-epithelialization period.

RESULTS

The formulated hydrogels were observed non-cytotoxic and compatible with human blood cells. No erythema and edema were found in skin irritation assay confirming the safety and applicability. Hydrogel consisting in a combination of amnion and collagen demonstrated significantly rapid wound healing, driven by complete re-epithelialization (16.75 ± 0.96 days) and closure by wound contraction (72 ± 3.27%, P < 0.0000009) when wound dressing membrane was used, whereas this gel alone healed about 62.5 ± 4.43% (P < 0.00001) and required 18.75 ± 0.50 days to complete re-epithelialization. Additionally, the gel with covering membrane treated group had maximum average body weight, food and water intake.

CONCLUSION

The amnion and collagen-based blended gel offers alternative possibilities to treat skin wounds when covered with film, which could overcome the limitations associated with modern therapeutic products such as high costs, long manufacturing times, complexities, storing, and presence of living biomaterials.

Insights into the physical properties and anisotropic nature of ErPdBi with an appearance of low minimum thermal conductivity

We theoretically study the structural, elastic and optical properties of ErPdBi together with its anisotropic behaviors using density functional theory. It is observed that ErPdBi satisfies the Born stability criteria nicely and possesses high quality of machinability. The anisotropic behavior of ErPdBi is reported with the help of theoretical anisotropy indices incorporating 3D graphical presentation, which suggests that ErPdBi is highly anisotropic in nature. It is noticed that the minimum thermal conductivity is very low for ErPdBi compared to the several species. This low value of minimum thermal conductivity introduces the potentiality of ErPdBi in high-temperature applications such as thermal barrier coatings. In addition, deep optical insights of ErPdBi reveal that our material can be used in different optoelectronic and electronic device applications ranging from organic light-emitting diodes, solar panel efficiency, waveguides etc. to integration of integrated circuits. Therefore, we believe that our results will provide a new insight into high-temperature applications and will benefit for the development of promising optoelectric devices as well.

The economic burden of measles in children under five in Bangladesh

BACKGROUND

This study estimated the economic cost of treating measles in children under-5 in Bangladesh from the caregiver, government, and societal perspectives.

METHOD

We conducted an incidence-based study using an ingredient-based approach. We surveyed the administrative staff and the healthcare professionals at the facilities, recording their estimates supported by administrative data from the healthcare perspective. We conducted 100 face-to-face caregiver interviews at discharge and phone interviews 7 to 14 days post-discharge to capture all expenses, including time costs related to measles. All costs are in 2018 USD ($).

RESULTS

From a societal perspective, a hospitalized and ambulatory case of measles cost $159 and $18, respectively. On average, the government spent $22 per hospitalized case of measles. At the same time, caregivers incurred $131 and $182 in economic costs, including $48 and $83 in out-of-pocket expenses in public and private not-for-profit facilities, respectively. Seventy-eight percent of the poorest caregivers faced catastrophic health expenditures compared to 21% of the richest. In 2018, 2263 cases of measles were confirmed, totaling $348,073 in economic costs to Bangladeshi society, with $121,842 in out-of-pocket payments for households.

CONCLUSION

The resurgence of measles outbreaks is a substantial cost for society, requiring significant short-term public expenditures, putting households into a precarious financial situation. Improving vaccination coverage in areas where it is deficient (Sylhet division in our study) would likely alleviate most of this burden.

Relationship of Plasma Vitamin-D Level with Left Ventricular Ejection Fraction in Patients with First Attack of Acute Myocardial Infarction

It has been widely reported that vitamin D deficiency is associated with Coronary heart disease (CHD), especially acute Myocardial infarction (MI). Many factors are responsible for reduced Left ventricular ejection fraction (LVEF) and acute Left ventricular fraction (LVF) after acute MI. This cross sectional descriptive type of study was conducted in the Cardiology department of Mymensingh Medical College Hospital from October 2017 to March 2019 to investigate the relationship of plasma vitamin D with LVEF in patients with first attack of acute MI. Total 185 patients of first attack of acute MI were included considering inclusion and exclusion criteria. Fasting blood samples were analyzed for plasma vitamin D level. Sample population were grouped at first into two, normal and low vitamin D level, taking 30ng/ml as cut-off value, low vitamin D level is further subdivided into insufficiency (21-29ng/ml), deficiency (10-20ng/ml) and severe deficiency (<10ng/ml). LVEF among the patients was observed. LVEF was found 49.88±8.58% patients having normal vitamin D level (>30ng/ml), 47.60±8.24% of patients having vitamin D insufficiency (21-29ng/ml), 44.38±8.12% of patients having vitamin D deficiency (10-20ng/ml) and 40.61±8.64% patients having severe vitamin D deficiency (<10ng/ml), which was statistically significant (p<0.05). So, low plasma vitamin D level is associated with reduced LVEF in patients hospitalized with first attack of acute MI.

Do employer-sponsored health insurance schemes affect the utilisation of medically trained providers and out-of-pocket payments among ready-made garment workers? A case-control study in Bangladesh

OBJECTIVE

We estimated the effect of an employer-sponsored health insurance (ESHI) scheme on healthcare utilisation of medically trained providers and reduction of out-of-pocket (OOP) expenditure among ready-made garment (RMG) workers.

DESIGN

We used a case-control study design with cross-sectional preintervention and postintervention surveys.

SETTINGS

The study was conducted among workers of seven purposively selected RMG factories in Shafipur, Gazipur in Bangladesh.

PARTICIPANTS

In total, 1924 RMG workers (480 from the insured and 482 from the uninsured, in each period) were surveyed from insured and uninsured RMG factories, respectively, in the preintervention (October 2013) and postintervention (April 2015) period.

INTERVENTIONS

We tested the effect of a pilot ESHI scheme which was implemented for 1 year.

OUTCOME MEASURES

The outcome measures were utilisation of medically trained providers and reduction of OOP expenditure among RMG workers. We estimated difference-in-difference (DiD) and applied two-part regression model to measure the association between healthcare utilisation, OOP payments and ESHI scheme membership while controlling for the socioeconomic characteristics of workers.

RESULTS

The ESHI scheme increased healthcare utilisation of medically trained providers by 26.1% (DiD=26.1; p<0.01) among insured workers compared with uninsured workers. While accounting for covariates, the effect on utilisation significantly reduced to 18.4% (p<0.05). The DiD estimate showed that OOP expenditure among insured workers decreased by -3700 Bangladeshi taka and -1100 Bangladeshi taka compared with uninsured workers when using healthcare services from medically trained providers or all provider respectively, although not significant. The multiple two-part models also reported similar results.

CONCLUSION

The ESHI scheme significantly increased utilisation of medically trained providers among RMG workers. However, it has no significant effect on OOP expenditure. It can be recommended that an educational intervention be provided to RMG workers to improve their healthcare-seeking behaviours and increase their utilisation of ESHI-designated healthcare providers while keeping OOP payments low.

Tunable anomalous Hall conductivity through volume-wise magnetic competition in a topological kagome magnet

Magnetic topological phases of quantum matter are an emerging frontier in physics and material science. Along these lines, several kagome magnets have appeared as the most promising platforms. Here, we explore magnetic correlations in the kagome magnet Co3Sn2S2. Using muon spin-rotation, we present evidence for competing magnetic orders in the kagome lattice of this compound. Our results show that while the sample exhibits an out-of-plane ferromagnetic ground state, an in-plane antiferromagnetic state appears at temperatures above 90 K, eventually attaining a volume fraction of 80% around 170 K, before reaching a non-magnetic state. Strikingly, the reduction of the anomalous Hall conductivity (AHC) above 90 K linearly follows the disappearance of the volume fraction of the ferromagnetic state. We further show that the competition of these magnetic phases is tunable through applying either an external magnetic field or hydrostatic pressure. Our results taken together suggest the thermal and quantum tuning of Berry curvature induced AHC via external tuning of magnetic order.

Fabrication of biocompatible porous scaffolds based on hydroxyapatite/collagen/chitosan composite for restoration of defected maxillofacial mandible bone

Fabrication of scaffolds from biomaterials for restoration of defected mandible bone has attained increased attention due to limited accessibility of natural bone for grafting. Hydroxyapatite (Ha), collagen type 1 (Col1) and chitosan (Cs) are widely used biomaterials which could be fabricated as a scaffold to overcome the paucity of bone substitutes. Here, rabbit Col1, shrimp Cs and bovine Ha were extracted and characterized with respect to physicochemical properties. Following the biocompatibility, degradability and cytotoxicity tests for Ha, Col1 and Cs a hydroxyapatite/collagen/chitosan (Ha·Col1·Cs) scaffold was fabricated using thermally induced phase separation technique. This scaffold was cross-linked with (1) either glutaraldehyde (GTA), (2) de-hydrothermal treatment (DTH), (3) irradiation (IR) and (4) 2-hydroxyethyl methacrylate (HEMA), resulting in four independent types (Ha·Col1·Cs-GTA, Ha·Col1·Cs-IR, Ha·Col1·Cs-DTH and Ha·Col1·Cs-HEMA). The developed composite scaffolds were porous with 3D interconnected fiber microstructure. However, Ha·Col1·Cs-IR and Ha·Col1·Cs-GTA showed better hydrophilicity and biodegradability. All four scaffolds showed desirable blood biocompatibility without cytotoxicity for brine shrimp. In vitro studies in the presence of human amniotic fluid-derived mesenchymal stem cells revealed that Ha·Col1·Cs-IR and Ha·Col1·Cs-DHT scaffolds were non-cytotoxic and compatible for cell attachment, growth and mineralization. Further, grafting of Ha·Col1·Cs-IR and Ha·Col1·Cs-DHT was performed in a surgically created non-load-bearing rabbit maxillofacial mandible defect model. Histological and radiological observations indicated the restoration of defected bone. Ha·Col1·Cs-IR and Ha·Col1·Cs-DHT could be used as an alternative treatment in bone defects and may contribute to further development of scaffolds for bone tissue engineering.

Technical efficiency of public district hospitals in Bangladesh: a data envelopment analysis

BACKGROUND

District hospitals (DHs) provide secondary level of healthcare to a wide range of population in Bangladesh. Efficient utilization of resources in these secondary hospitals is essential for delivering health services at a lower cost. Therefore, we aimed to estimate the technical efficiency of the DHs in Bangladesh.

METHODS

We used input-oriented data envelopment analysis method to estimate the variable returns to scale (VRS) and constant returns to scale (CRS) technical efficiency of the DHs using data from Local Health Bulletin, 2015. In this model, we considered workforce as well as number of inpatient beds as input variables and number of inpatient, outpatient, and maternal services provided by the DHs as output variables. A Tobit regression model was applied for assessing the association of institutional and environmental characteristics with the technical efficiency scores.

RESULTS

The average scale, VRS, and CRS technical efficiency of the DHs were estimated to 85%, 92%, and 79% respectively. Population size, poverty headcount, bed occupancy ratio, administrative divisions were significantly associated with the technical efficiency of the DHs. The mean VRS and CRS technical efficiency demonstrated that the DHs, on an average, could reduce their input mix by 8% and 21% respectively while maintaining the same level of output.

CONCLUSION

Since the average technical efficiency of the DHs was 79%, there is little scope for overall improvements in these facilities by adjusting inputs. Therefore, we recommend to invest further in the DHs for improvement of services. The Ministry of Health and Family Welfare (MoHFW) should improve the efficiency in resource allocation by setting an input-mix formula for DHs considering health and socio-economic indicators (e.g., population density, poverty, bed occupancy ratio). The formula can be designed by learning from the input mix in the more efficient DHs. The MoHFW should conduct this kind of benchmarking study regularly to assess the efficiency level of health facilities which may contribute to reduce the wastage of resources and consequently to provide more affordable and accessible public hospital care.

Human tissue banking in Bangladesh: hope for the patients of massive burns, surgical wound and bone associated complications

Each year throughout Bangladesh, thousands of people suffering from massive burns and surgical wounds require amniotic grafts for transplantation. Additionally, the stricken persons of the country have to embrace bone associated disability for the whole life due to traumatic complications need bone graft to treat. As a result, these two problems are the largest financial burden as this situation not only affect the family of patients but also cripple down national economy. However, institute of tissue banking in Bangladesh has undertaken the service program on the processing, preservation and clinical applications of amnion membrane and bone graft for rehabilitative surgery. Importantly, in recent years, this institute has started cranial bone autograft processing and transplantation. In accidental cases such as head injury, it is difficult to provide suitable cranial bone allograft according to demand. In this situation, injured cranial bone of the patient is being transported to the lab of the institute, where the scientist, tissue banker and medic work together immediately to process the cranial bone and sterilize by gamma radiation; and after quality assurance, the processed cranial bone autograft is being supplied for replacement surgery. The use of irradiated amnion and bone allografts and cranial bone autograft in reconstructive surgery restore normalcy to lives of many patients from disabilities. This tissue bank is based on finding and obtaining qualified donors from the community and a demand for tissue grafts from the hospitals. Although growing needs for tissue transplantation but raw and processed tissue grafts preservation and banking braces enormous logistical limitations. The only human tissue bank in Bangladesh, however, ensures the availability of tissue allografts of high quality and acceptability to the recipients for rehabilitative surgery for a decade, regardless patients' socio-economic status.

Measuring the efficiency of health systems in Asia: a data envelopment analysis

OBJECTIVE

This study aims to estimate the technical efficiency of health systems in Asia.

SETTINGS

The study was conducted in Asian countries.

METHODS

We applied an output-oriented data envelopment analysis (DEA) approach to estimate the technical efficiency of the health systems in Asian countries. The DEA model used per-capita health expenditure (all healthcare resources as a proxy) as input variable and cross-country comparable health outcome indicators (eg, healthy life expectancy at birth and infant mortality per 1000 live births) as output variables. Censored Tobit regression and smoothed bootstrap models were used to observe the associated factors with the efficiency scores. A sensitivity analysis was performed to assess the consistency of these efficiency scores.

RESULTS

The main findings of this paper demonstrate that about 91.3% (42 of 46 countries) of the studied Asian countries were inefficient with respect to using healthcare system resources. Most of the efficient countries belonged to the high-income group (Cyprus, Japan, and Singapore) and only one country belonged to the lower middle-income group (Bangladesh). Through improving health system efficiency, the studied high-income, upper middle-income, low-income and lower middle-income countries can improve health system outcomes by 6.6%, 8.6% and 8.7%, respectively, using the existing level of resources. Population density, bed density, and primary education completion rate significantly influenced the efficiency score.

CONCLUSION

The results of this analysis showed inefficiency of the health systems in most of the Asian countries and imply that many countries may improve their health system efficiency using the current level of resources. The identified inefficient countries could pay attention to benchmarking their health systems within their income group or other within similar types of health systems.

Inequalities in the prevalence of undiagnosed hypertension among Bangladeshi adults: evidence from a nationwide survey

Background

In recent years, developing countries like Bangladesh are facing a higher burden of non-communicable diseases such as hypertension as a result of demographic transition. Prevalence of hypertension is often studied in this setting. However, evidence on undiagnosed hypertension is not widely available in the existing literature. Therefore, the current study focuses on inequalities in the prevalence of undiagnosed hypertension in Bangladesh.

Methods

A total of 8835 participants aged 35+ years were included in this study using nationally representative Bangladesh Demographic and Health Survey 2011 (BDHS). In the survey, systolic blood pressure (SBP) and diastolic blood pressure (DBP) of these participants were measured three times with approximately 10 minutes of an interval between each measurement. Any respondent with either SBP ≥ 140 mmHg or DBP ≥ 90 mmHg was considered as patient with hypertension as per the guidelines from American Heart Association. Among the participants, undiagnosed hypertension was defined as having SBP > =140 mmHg or DBP > =90 mmHg and never taking prescribed medicine or being told by health professionals to lower/control blood pressure. Multiple logistic regression analysis was applied for identifying factors associated with undiagnosed hypertension. Further, socioeconomic inequalities in the prevalence of undiagnosed hypertension were estimated using Concentration Index (C).

Results

We found 978 (59.9% of the total) were undiagnosed among 1685 hypertensive patients studied. Regression analysis showed individuals with being underweight, having poor socioeconomic conditions, and lower educational qualifications were more likely to have undiagnosed hypertension. A similar association between undiagnosed hypertension and socioeconomic quintiles was observed using concentration index (C = − 0.07). On the other hand, individuals from higher age group (50–64 or above), female sex, and Sylhet region were at lower risk of undiagnosed hypertension.

Conclusions

This study showed that a large proportion of the cases with hypertension are remained undiagnosed in Bangladesh, especially among the poor and low educated population. Screening and awareness building initiatives on hypertension should be taken for this group of population to reduce the burden of undiagnosed hypertension.