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Background and objectives: Thalassaemia is an inherited life-threatening but preventable haemoglobin disorder. South Asian countries, including Bangladesh, are the hotspots of the world's thalassaemia belt. Indigenous communities are underprivileged and vulnerable to genetic disorders, including thalassaemia. Understanding the perspectives of thalassaemia of future community leaders (indigenous university students) is critical for developing a tailor-made preventive strategy relevant to their communities. In this study, we aimed to assess the level of knowledge and attitudes towards thalassaemia among indigenous university students and determine their thalassaemia carrier status. Methods: A cross-sectional survey was conducted among 251 tribal university students using a published questionnaire between May and October 2018. The main survey instrument consisted of 22 anonymous questions. Descriptive and inferential statistical procedures were used for data analysis. Results: More than half (55%) of the indigenous students had never heard the term 'thalassaemia'. Around half (49%) of the marriages in their communities were consanguineous. The mean knowledge score was abysmal (4.91±2.65 out of a 12-point scale), which was not associated with the consanguinity of their parent but home districts. Multiple linear regression of demographic variables on the total knowledge score revealed that the overall knowledge is significantly associated with their home district (p< 0.05). Participants from science disciplines scored more than 1 point than their counterparts from Arts and Humanities (p = 0.08615). Conclusions: For the first time, this study has identified knowledge gaps and misperceptions about thalassaemia among university students from indigenous communities in the southeastern region of Bangladesh. This study serves as a baseline for future interventions (premarital and prenatal screening) targeting future community leaders. [ABSTRACT FROM AUTHOR]

The textile wet processing industry is considered a notorious polluter due to its widespread use of natural resources without proper replenishment. Bangladesh is one of the largest producers of textile products in the World, and therefore, it is vulnerable to environmental degradation. Bangladesh is predominantly a cotton processing country; therefore, reactive dyes are commonly used, and the dye combination is very water and chemical-intensive. There is a scarcity of information on the consumption of water, textile dyes and the generation of wastewater in the textile sector. Thus, this study aimed to estimate the amount of water use, wastewater generation, and chemical use in textile wet processing units. Therefore, a faceto- face in-depth questionnaire-based survey was conducted in 18 textile wet processing factories, including knit composite, knit dyeing, yarn dyeing, denim dyeing, and knit and yarn dyeing. The average specific groundwater consumption to process 1 Kg of textile materials was 164 L/Kg (SD ~ 81.8); dyehouse water was136 L/Kg (SD ~ 70.6), while corresponding wastewater was 119 L/Kg (SD ~ 73.0). This high consumption of groundwater is directly linked to the depletion of groundwater in the region, where textile industries are situated and also, causes water pollution through wastewater generation. The water used in the dyehouse water was usually soft water and found to be in a range of 68% to 100% that of groundwater extracted. For chemical use, a factory used 449 g of chemicals to process 1 Kg of textile materials, in which the most widely used chemicals were inorganic and basic chemical in nature. However, the chemical use varied from 152 g/Kg to 705 g/Kg of textile production. The total chemical consumption ranged from 954 tons to 4,525 tons a year. More than 50% of the wastewater treatment plants were biological, a quarter of combination and physico- chemical and biological, and the rest were chemical treatment plants in this study. Even though this study may not represent the whole textile wet processing industry of Bangladesh, however, This study provides baseline information on water and chemical consumption and wastewater generation. Our findings would be helpful for policy makers and researchers to identify transformative challenges required at the national level. [ABSTRACT FROM AUTHOR]

Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early. Author summary: Dengue is a mosquito-borne viral infection mostly in warm and tropical regions, which has been listed as one of the top ten global health threats by the WHO. Among neglected tropical diseases, the mortality of dengue is on the rise. Severe dengue (typically manifested by bleeding, organ dysfunction, and plasma leakage) has become a leading cause of hospitalization for children and adults. There is a higher risk of death if severe dengue cases are not appropriately managed. Therefore, finding biomarkers that can reliably predict the development of severe dengue in symptomatic individuals is one of the main focuses of current research efforts. We found that dyspnoea, plasma leakage, and hemorrhage were the independent risk factors of severe dengue. The predictive probability of occurrence of severe dengue achieved 92.9% among people aged ≤ 12.5 years with dyspnoea and plasma leakage. Establishing an early warning system for severe dengue based on these factors is essential for triaging in endemic areas. The findings of this study identified possible combinations of severe dengue, which would provide enhanced insight into clinical management and inform prevention programming for severe dengue. [ABSTRACT FROM AUTHOR]

Clinical Profile, Haematological Changes and Outcomes of Dengue Patients in Dengue Outbreak-2019 in Jashore, Bangladesh- An Observational Study. Graph: Fig 1 Trend of dengue and COVID-19 case reporting.(A) Number month-wise hospitalized dengue cases 2020 and 2021. Bangladesh, a South Asian country with over 165 million people, is currently confronting the worst phase of the Coronavirus Disease 2019 (COVID-19) pandemic since the official declaration on 8 March 2020, with a record surge in cases and deaths. [Extracted from the article]

Efforts to mitigate public health concerns are showing encouraging results over the time but disparities across the geographic regions still exist within countries. Inadequate researches on the regional disparities of health indicators based on representative and comparable data create challenges to develop evidence-based health policies, planning and future studies in developing countries like Bangladesh. This study examined the disparities among districts on various maternal and child health indicators in Bangladesh. Cluster analysis–an unsupervised learning technique was used based on nationally representative dataset originated from Multiple Indicator Cluster Survey (MICS), 2012–13. According to our results, Bangladesh is classified into two clusters based on different health indicators with substantial variations in districts per clusters for different sets of indicators suggesting regional variation across the indicators. There is a need to differentially focus on community-level interventions aimed at increasing maternal and child health care utilization and improving the socioeconomic position of mothers, especially in disadvantaged regions. The cluster analysis approach is unique in terms of the use of health care metrics in a multivariate setup to study regional similarity and dissimilarity in the context of Bangladesh. [ABSTRACT FROM AUTHOR]

Background: Chikungunya virus causes mosquito-transmitted infection that leads to extensive morbidity affecting substantial quality of life. Disease associated morbidity, quality of life, and financial loss are seldom reported in resources limited countries, such as Bangladesh. We reported the acute clinical profile, quality of life and consequent economic burden of the affected individuals in the recent chikungunya outbreak (May to September 2017) in Dhaka city, Bangladesh. Methods: We conducted a cross-sectional study during the peak of chikungunya outbreak (July 24 to August 5, 2017) to document the clinical profiles of confirmed cases (laboratory test positive) and probable cases diagnosed by medical practitioners. Data related to clinical symptoms, treatment cost, loss of productivity due to missing work days, and quality of life during their first two-weeks of symptom onset were collected via face to face interview using a structured questionnaire. World Health Organization endorsed questionnaire was used to assess the quality of life. Results: A total of 1,326 chikungunya cases were investigated. Multivariate analysis of major clinical variables showed no statistically significant differences between confirmed and probable cases. All the patients reported joint pain and fever. Other more frequently reported symptoms include headache, loss of appetite, rash, myalgia, and itching. Arthralgia was polyarticular in 56.3% of the patients. Notably, more than 70% patients reported joint pain as the first presenting symptom. About 83% of the patients reported low to very low overall quality of life. Nearly half of the patients lost more than 10 days of productivity due to severe arthropathy. Conclusions: This study represents one of the largest samples studied so far around the world describing the clinical profile of chikungunya infection. Our findings would contribute to establish an effective syndromic surveillance system for early detection and timely public health intervention of future chikungunya outbreaks in resource-limited settings like Bangladesh. [ABSTRACT FROM AUTHOR]

Measuring COVID-19-related mortality is vital for making public health policy decisions. The magnitude of COVID-19-related mortality is largely unknown in low- and middle-income countries (LMICs), including Bangladesh, due to inadequate COVID-19 testing capacity and a lack of robust civil registration and vital statistics systems. Even with the lack of data, cemetery-based death records in LMICs may provide insightful information on potential COVID-19-related mortality rates; nevertheless, there is a dearth of research employing cemetery-based death records. This study aimed to assess the excess mortality during the COVID-19 pandemic in an urban setting in Bangladesh using a cemetery-based death registration dataset. A total of 6,271 deaths recorded between January 2015 and December 2021 were analysed using a Bayesian structural time series model. Exploratory analysis found that the average monthly number of deaths was 69 during the pre-COVID-19 period (January 2015-February 2020), but significantly increased to 92 during the COVID-19 period (March 2020-December 2021). The increase in male deaths was twice as large as the increase in female deaths. Model-based results were not statistically significant (relative effect 17%, 95% credible interval: -18%, 57%), but there was an overall increasing trend during the COVID-19 period, and specific months or shorter periods had a substantial increase. This first-of-its-kind study in Bangladesh has assessed the excess mortality in an urban community during the COVID-19 pandemic. Cemetery-based death registration appears to aid in tracking population mortality, especially in resource-limited countries where collecting data on the ground is challenging during crisis periods; however, additional large-scale research is required., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hossain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)