ABSTRACT
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.
ABSTRACT
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 (ß = 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.
ABSTRACT
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.
ABSTRACT
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.
Subject(s)
COVID-19 Drug Treatment , Poverty Areas , Bangladesh/epidemiology , Communicable Disease Control , Cross-Sectional Studies , Female , Humans , SARS-CoV-2 , Urban PopulationABSTRACT
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.