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Correction to: Water, sanitation and hygiene (WASH) practices and deworming improve nutritional status and anemia of unmarried adolescent girls in rural Bangladesh (Journal of Health, Population and Nutrition, (2023), 42, 1, (127), 10.1186/s41043-023-00453-8)
(BioMed Central Ltd, 2024-12-01) Jolly, Saira Parveen; Chowdhury, Tridib Roy; Sarker, Tanbi Tanaya; Afsana, Kaosar; BRAC James P Grant School of Public Health
Following publication of the original article [1], the authors identified errors in Tables 2 and 3. The symbol ± appeared twice in Table 2 between mean and (95%.) where it shouldn’t have been indicated. The sub-header Mean ± SD was missing from the Table 3 sub-header. The incorrect Table 2: Dietary diversity and nutrients intake and of the adolescent girls by study areas Variables p-value Comparison n = 809 Average number of food groups intake during last 24 hrs, Mean ± SD** 3.91 ± 1.25 3.97 ± 1.24 0.889 Dietary diversity score during last 24 hrs, n (%) * 1 ‒3 food groups (low) 37.85(307) 38.44(311) 0.431 4 ‒6 food groups (average) 59.93(486) 58.54(472) 7 ‒10 food groups (high) 2.22(18) 3.21(26) Vitamin A rich dark green leafy vegetable, n (%) * 20.5 (166) 30.3 (245) 0.000 Organ meat, n (%) * 1.5 (12) 1.2 (10) 0.672 Fish, meat, poultry, n (%) * 73.7 (598) 74.8 (605) 0.630 Energy, in kcal/day, Mean(95% CI)** 1344.29(1357.64-1418.94) 1403.61(1375.78-1431.43) 0.468 Protein, in g/day, Mean(95% CI)** 45.61(44.37–46.85) 46.36(45.26–47.46) 0.375 Fat, in g/day, Mean (95% CI) ** 14.28(13.78–14.78) 14.28(13.77–14.29) 0.998 Carbohydrate, in g/day, Mean (95% CI) ** 260.37(254.64-266.11) 264.34(258.83-269.84) 0.328 Calcium, in mg/day, Mean (95% CI) ** 635.38(550.01-720.75) 745.07(667.48–822.30) 0.062 Iron, in g/day, Mean (95% CI) ** 7.78(7.5–8.07) 8.65(8.22–9.07) 0.001 Zinc, in mg/day, Mean (95% CI) ** 8.85(8.31–8.78) 8.68(8.44–9.07) 0.445 Vitamin A, in µg/day, Mean± (95% CI) ** 167.95(151.88-184.02) 233.01(207.14-258.19) 0.000 Thiamin, in mg/day, Mean (95% CI) ** 1.19(1.16–1.22) 1.18(1.15–1.21) 0.801 Riboflavin, in mg/day, Mean (95% CI) ** 0.64(0.61–0.67) 0.67(0.62–0.71) 0.365 Vitamin C, in mg/day, Mean± (95% CI) ** 65.00(61.06–68.93) 71.09(67.04–75.14) 0.034 Intake of iron supplement during last one month, %(n)* 5.2 (42) 4.4(36) 0.493 Frequency of taking iron supplement, %(n)* Daily 23.8(10) 38.9(14) 0.322 7 days 33.3(14) 30.6(11) < 7 days 42.9(18) 30.6(11) *Chi-square test **Student t-test Hrs = Hours The correct Table 2: Dietary diversity and nutrients intake and of the adolescent girls by study areas Variables p-value Comparison n = 809 Average number of food groups intake during last 24 hrs, Mean ± SD** 3.91 ± 1.25 3.97 ± 1.24 0.889 Dietary diversity score during last 24 hrs, n (%) * 1 ‒3 food groups (low) 37.85(307) 38.44(311) 0.431 4 ‒6 food groups (average) 59.93(486) 58.54(472) 7 ‒10 food groups (high) 2.22(18) 3.21(26) Vitamin A rich dark green leafy vegetable, n (%) * 20.5 (166) 30.3 (245) 0.000 Organ meat, n (%) * 1.5 (12) 1.2 (10) 0.672 Fish, meat, poultry, n (%) * 73.7 (598) 74.8 (605) 0.630 Energy, in kcal/day, Mean(95% CI)** 1344.29(1357.64-1418.94) 1403.61(1375.78-1431.43) 0.468 Protein, in g/day, Mean(95% CI)** 45.61(44.37–46.85) 46.36(45.26–47.46) 0.375 Fat, in g/day, Mean (95% CI) ** 14.28(13.78–14.78) 14.28(13.77–14.29) 0.998 Carbohydrate, in g/day, Mean (95% CI) ** 260.37(254.64-266.11) 264.34(258.83-269.84) 0.328 Calcium, in mg/day, Mean (95% CI) ** 635.38(550.01-720.75) 745.07(667.48–822.30) 0.062 Iron, in g/day, Mean (95% CI) ** 7.78(7.5–8.07) 8.65(8.22–9.07) 0.001 Zinc, in mg/day, Mean (95% CI) ** 8.85(8.31–8.78) 8.68(8.44–9.07) 0.445 Vitamin A, in µg/day, Mean (95% CI) ** 167.95(151.88-184.02) 233.01(207.14-258.19) 0.000 Thiamin, in mg/day, Mean (95% CI) ** 1.19(1.16–1.22) 1.18(1.15–1.21) 0.801 Riboflavin, in mg/day, Mean (95% CI) ** 0.64(0.61–0.67) 0.67(0.62–0.71) 0.365 Vitamin C, in mg/day, Mean (95% CI) ** 65.00(61.06–68.93) 71.09(67.04–75.14) 0.034 Intake of iron supplement during last one month, %(n)* 5.2 (42) 4.4(36) 0.493 Frequency of taking iron supplement, %(n)* Daily 23.8(10) 38.9(14) 0.322 7 days 33.3(14) 30.6(11) < 7 days 42.9(18) 30.6(11) *Chi-square test **Student t-test Hrs = Hours The incorrect Table 3: Nutritional status of the adolescent girls by study area Variables Study area p-value Intervention n = 811 Comparison n = 809 Weight in kg 38.36 ± 8.29 38.50 ± 8.85 0.742 Height in cm 146.57 ± 8.26 146.56 ± 8.46 0.985 a MAC in mm 213.56 ± 28.49 213.42 ± 29.72 0.923 b BMI in kg/m2 17.70 ± 2.76 17.74 ± 2.98 0.793 c HAZ- score -1.27 ± 1.07 1.24 ± 1.05 0.516 d BMIZ 0.71 ± 1.07 0.72 ± 1.11 0.982 Hb in g/dl 12.4 ± 1.3 12.3 ± 1.3 0.529 **Student t test aMid arm circumferences b BMI = Body Mass Index cHeight-for-age Z score dBMI-for-age Z score The correct Table 3: Average nutritional status of adolescent girls by study area Variables Study area p-value Intervention n = 811 Comparison n = 809 Mean ± SD Mean ± SD Weight in kg 38.36 ± 8.29 38.50 ± 8.85 0.742 Height in cm 146.57 ± 8.26 146.56 ± 8.46 0.985 a MAC in mm 213.56 ± 28.49 213.42 ± 29.72 0.923 b BMI in kg/m2 17.70 ± 2.76 17.74 ± 2.98 0.793 c HAZ- score -1.27 ± 1.07 1.24 ± 1.05 0.516 d BMIZ 0.71 ± 1.07 0.72 ± 1.11 0.982 Hb in g/dl 12.4 ± 1.3 12.3 ± 1.3 0.529 **Student t test aMid arm circumferences b BMI = Body Mass Index cHeight-for-age Z score dBMI-for-age Z score The correct Tables 2 and 3 have been indicated in this correction article and the original article [1] has been corrected. © The Author(s) 2024.
SensaNet: a lightweight DL model for tuberculosis detection in histopathological images
(Institute of Electrical and Electronics Engineers Inc., 2025-01-01) Mahtab M.A.; Tasnim, Sanjida; Choudhury M.S.; Wasi S.; Bhuiyan S.T.; Alam S.B.; Rahman R.; School of Data & Sciences, BRAC University
Tuberculosis remains a global health problem, particularly in resource-limited settings in which early and accurate diagnosis is paramount. This research presents SensaNet, an efficient yet light-weight binary tuberculosis (TB) classification for histopathological image patches. SensaNet is evaluated against a well-curated set of 27,987 Kinyoun-stained image patches that were scanned from digitized slides of sputum smears, with wellbalanced bacilli-positive and negative distributions. SensaNet combines current architectural innovations such as Squeeze-and-Excitation (SE) blocks, Swish activation, and single-head self-attention to amplify feature representation in channel and spatial domains with lower memory cost. Experimental results confirm that SensaNet achieves an accuracy of 98.54%, precision of 98.19%, and recall of 98.95%, outperforming several baseline architectures on sensitivity with the compact size of 12.2 MB and only above 1 million parameters. Comparative analysis proves SensaNet's suitability for TB diagnosis, offering a good trade-off between diagnostic performance and computational expense. These results weigh in favor of the model's potential deployment for real-time point-of-care diagnostics, particularly for low-resource environments.
Determinants of health seeking behavior for chronic non-communicable diseases and related out-of-pocket expenditure: Results from a cross-sectional survey in northern Bangladesh
(BioMed Central Ltd., 2019-12-23) Rasul, Fatema Binte; Kalmus, Olivier; Sarker, Malabika; Adib, Hossain Ishrath; Hossain, Md Shahadath; Hasan, Md Zabir; Brenner, Stephan; Nazneen, Shaila; Islam, Muhammed Nazmul; De Allegri, Manuela; BRAC James P Grant School of Public Health
Background: In spite of high prevalence rates, little is known about health seeking and related expenditure for chronic non-communicable diseases in low-income countries. We assessed relevant patterns of health seeking and related out-of-pocket expenditure in Bangladesh. Methods: We used data from a household survey of 2500 households conducted in 2013 in Rangpur district. We employed multinomial logistic regression to assess factors associated with health seeking choices (no care or self-care, semi-qualified professional care, and qualified professional care). We used descriptive statistics (5% trimmed mean and range, median) to assess related patterns of out-of-pocket expenditure (including only direct costs). Results: Eight hundred sixty-six (12.5%) out of 6958 individuals reported at least one chronic non-communicable disease. Of these 866 individuals, 139 (16%) sought no care or self-care, 364 (42%) sought semi-qualified care, and 363 (42%) sought qualified care. Multivariate analysis confirmed that the following factors increased the likelihood of seeking qualified care: A higher education, a major chronic non-communicable disease, a higher socio-economic status, a lower proportion of chronic household patients, and a shorter distance between a household and a sub-district public referral health facility. Seven hundred fifty-four (87 %) individuals reported out-of-pocket expenditure, with drugs absorbing the largest portion (85%) of total expenditure. On average, qualified care seekers encountered the highest out-of-pocket expenditure, followed by those who sought semi-qualified care and no care, or self-care. Conclusion: Our study reveals insufficiencies in health provision for chronic conditions, with more than half of all affected people still not seeking qualified care, and the majority still encountering considerable out-of-pocket expenditure. This calls for urgent measures to secure better access to care and financial protection. © 2019 The Author(s).
Role of spatial tools in public health policymaking of Bangladesh: opportunities and challenges
(Journal of Health, Population and Nutrition, 2016-02-27) Kim, Dohyeong; Sarker, Malabika; Vyas, Priyanka; BRAC James P Grant School of Public Health
In spite of the increasing efforts to gather spatial data in developing countries, the use of maps is mostly for visualization of health indicators rather than informed decision-making. Various spatial tools can aid policymakers to allocate resources effectively, predict patterns in communicable or infectious diseases, and provide insights into geographical factors which are associated with utilization or adequacy of health services. In Bangladesh, the launch of District Health Information System 2, along with recent efforts to gather spatial data of facilities location, provides an interesting opportunity to study the current landscape and the potential barriers in advancing the use of spatial tools for informed decision making. This study assessed the current level of map usage and spatial tools for health sector planning in Bangladesh, focusing on investigating why map usage and spatial tools remained at a basic level for the purpose of health policy. The study design involved in-depth interviews, followed by an expert survey (n = 39) obtained through snowball sampling.Our survey revealed that assessing areas with shortage of community health workers emerged as the top most for basic map usage or primarily for visualization purpose, while planning for emergency and obstetric care services, and disease mapping was the most frequent category for intermediate and advanced map usage, respectively. Furthermore, we found lack of inter-institutional collaboration, lack of continuous availability of trained personnel, and lack of awareness on the use of geographic information system (GIS) as a decision-making tool as three most critical barriers in the current landscape. Our findings highlight the barriers in increasing the adoption of spatial tools for health policymaking and planning in Bangladesh.
Eczema and Seborrheic Keratoses: a novel method for skin disease classification using image-based analysis
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Sakib M.; Asadullah M.; Shams, Sharif Mohd; Hossain M.D.; Uddin M.M.; Hossain M.M.; Department of Electrical and Electronics Engineering
The human skin, which is the biggest organ and the outermost layer of the body, has seven layers that serve to shield interior organs. Because of its broad role in the integumentary system, maintaining the health of the skin is essential. Skin problems present substantial classification challenges for medical professionals since they include a wide spectrum of diseases, including dermatoses. Consequently, they are depending more and more on machine learning (ML) technologies to help them predict and categorize these diseases. In the field of imaging, convolutional neural networks (CNNs) have demonstrated performance that is comparable to, and in some cases surpasses, human capabilities. Within this research, we propose a novel CNN architecture designed to classify two specific skin diseases: Eczema (symptoms on legs and hands) and Seborrheic Keratoses (symptoms on ears and skin). Additionally, we compare the performance of six ML algorithms to determine the most accurate model. We trained and tested our proposed technique on the Dermnet 2021 DATASET, which consists of 2,332 pictures and is publically available on Kaggle. Our findings show that the suggested CNN model, which achieves an accuracy of 91.1% and an F1-score of 92.3%, surpasses other cutting-edge techniques. With an F1-score of 79.12% and an accuracy of 78.41%, Linear Regression (LR) was the most successful ML model that was examined.