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The global Coronavirus disease (COVID-19) pandemic disrupted healthcare systems, reducing access to medical services. In Bangladesh, strict lockdowns, healthcare worker shortages, and resource diversion further strained the system. Despite these challenges, the impact on inpatient and outpatient service utilisation in Bangladesh remains unaddressed. This study explored the levels of inpatient admissions and outpatient visits in public healthcare facilities before and during COVID-19 pandemic in Bangladesh. We conducted a cross-sectional secondary analysis of inpatient and outpatient data from all public hospitals collected via District Health Information System, version 2 (DHIS2) from January 2017 to June 2021. Using 2017-2019 as the baseline, we analysed healthcare utilisation indicators (outpatient visits and inpatient admissions) with descriptive and segmented Poisson regression to assess the impact of COVID-19 in 2020 and 2021. In 2020, outpatient visits and inpatient admissions significantly declined to 34.1 million and 37.5 million, respectively, from 47.6 million and 56.2 million in 2019. Segmented regression analysis confirmed these drops, especially in Dhaka (IRR =  0.62, p < 0.001) and Barisal (IRR =  0.69, p < 0.002) for outpatient visits, and in Dhaka (IRR =  0.64, p < 0.000) and Khulna (IRR =  0.70, p < 0.000) for inpatient admissions. In 2021, most divisions saw an increase in outpatient visit and inpatient admission numbers, with the lowest rebound in Sylhet. The COVID-19 pandemic significantly reduced Outpatient Department (OPD) visits and Inpatient Department (IPD) admissions in Bangladesh in 2020, with partial recovery in 2021. To ensure sustained access to care, it is crucial to strengthen healthcare facilities and equip healthcare providers to be prepared for future pandemics or emergencies.

Increasing the availability and readiness of basic emergency obstetric and newborn care (BEmONC) services is essential for improving maternal and neonatal health. However, little is known about any progress made in the availability and readiness of BEmONC services in Bangladesh. Using nationally representative data from the Bangladesh Health Facility Survey conducted between 2014 and 2017, we measured changes in the availability and readiness of BEmONC services in health facilities in Bangladesh, calculating the BEmONC service availability and readiness scores according to the World Health Organization Service Availability and Readiness Assessment guideline. The percentage of health facilities performing all seven basic signal functions declined slightly from 13% in 2014 to 11% in 2017. The decline was largely noticed in Maternal and Child Welfare Centers, Upazila Health Complexes, and Union Subcenter/Rural Dispensaries, as well as in all divisions except Rangpur. No remarkable changes in overall readiness of health facilities across location, division and facility type were observed between 2014 and 2017. However, significant reductions in availability and readiness were noticed when item-specific assessment was made. Type of health facility was significantly associated with both availability and readiness scores in adjusted regression models. Appropriate strategies and efforts could improve the availability and readiness of BEmONC services in health facilities in Bangladesh.

Technological innovations and quality control processes within blood supply organizations have significantly improved blood safety for both donors and recipients. Nevertheless, the risk of transfusion-transmitted infection remains non-negligible. Applying a nanoparticular, antibacterial coating at the surface of medical devices is a promising strategy to prevent the spread of infections. In this study, we characterized the antibacterial activity of an SiO2 nanoparticular coating (i.e., the “Medical Antibacterial and Antiadhesive Coating” [MAAC]) applied on relevant polymeric materials (PM) used in the biomedical field. Electron microscopy revealed a smoother surface for the MAAC-treated PM compared to the reference, suggesting antiadhesive properties. The antibacterial activity was tested against selected Gram-positive and Gram-negative bacteria in accordance with ISO 22196. Bacterial growth was significantly reduced for the MAAC-treated PVC, plasticized PVC, polyurethane and silicone (90–99.999%) in which antibacterial activity of ≥1 log reduction was reached for all bacterial strains tested. Cytotoxicity was evaluated following ISO 10993-5 guidelines and L929 cell viability was calculated at ≥90% in the presence of MAAC. This study demonstrates that the MAAC could prevent bacterial contamination as demonstrated by the ISO 22196 tests, while further work needs to be done to improve the coating processability and effectiveness of more complex matrices.

Hypothermia remains a leading contributing factor to neonatal mortality. This study reports testing of a thermoregulatory device—‘Thermal Jacket’ that includes a reusable chemical warming pad (CWP) and an insulating jacket designed for hypothermia management. The laboratory experiments were conducted in two distinct phases between February’21 and June’22. In phase 1, a ternary composite of Sodium-Acetate-Trihydrate, Glycerol, Paraffin, and water contained in a high-density polyethylene-pouch named ‘CWP’ was finalised, and an insulating jacket was designed for targeted heat retention. In phase 2, the device’s efficacy was evaluated using a mannequin in a controlled setting. The sample size was 81 events. Welch’s t-test, ANOVA, and GEE were used to assess any significant differences between successful and failed events. Among 81 events, approximately 93% events of CWP and 98% events of insulating jacket successfully maintained temperature within 36–38°C for 120 minutes. Moreover, ambient temperature, reuse of CWPs, humidity did not have any significant effect on the success rate of the CWP and insulating jacket. Thermal Jacket had achieved and sustained the temperature range of 36–38°C for 2 hours. While this study used mannequin, clinical trial with preterm or low birthweight neonates is imperative to assess its effectiveness for thermal care management.

The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.

Background The global outbreak of COVID-19 has created unprecedented havoc among health care workers, resulting in significant psychological strains like insomnia. This study aimed to analyze insomnia prevalence and job stressors among Bangladeshi health care workers in COVID-19 units. Methodology We conducted this cross-sectional study to assess insomnia severity from January to March 2021 among 454 health care workers working in multiple hospitals in Dhaka city with active COVID-dedicated units. We selected 25 hospitals conveniently. We used a structured questionnaire for face-to-face interviews containing sociodemographic variables and job stressors. The severity of insomnia was measured by the Insomnia Severity Scale (ISS). The scale has seven items to evaluate the rate of insomnia, which was categorized as the absence of Insomnia (0–7); sub-threshold Insomnia (8–14); moderate clinical Insomnia (15–21); and severe clinical Insomnia (22–28). To identify clinical insomnia, a cut-off value of 15 was decided primarily. A cut-off score of 15 was initially proposed for identifying clinical insomnia. We performed a chi-square test and adjusted logistic regression to explore the association of different independent variables with clinically significant insomnia using the software SPSS version 25.0. Results 61.5% of our study participants were females. 44.9% were doctors, 33.9% were nurses, and 21.1% were other health care workers. Insomnia was more dominant among doctors and nurses (16.2% and 13.6%, respectively) than others (4.2%). We found clinically significant insomnia was associated with several job stressors (p < 0.05). In binary logistic regression, having sick leave (OR = 0.248, 95% CI = 0.116, 0.532) and being entitled to risk allowance (OR = 0.367, 95% CI = 0.124.1.081) showed lower odds of developing Insomnia. Previously diagnosed with COVID-19-positive health care workers had an OR of 2.596 (95% CI = 1.248, 5.399), pointing at negative experiences influencing insomnia. In addition, we observed that any training on risk and hazard increased the chances of suffering from Insomnia (OR = 1.923, 95% CI = 0.934, 3.958). Conclusion It is evident from the findings that the volatile existence and ambiguity of COVID-19 have induced significant adverse psychological effects and subsequently directed our HCWs toward disturbed sleep and insomnia. The study recommends the imperativeness to formulate and implement collaborative interventions to help HCWs cope with this crisis and mitigate the mental stresses they experience during the pandemic.

The coronavirus disease 2019 (COVID-19) pandemic had substantially disrupted maternal health care provision and utilisation in Bangladesh. However, the extent of geographical disparities in service utilisation and how the health system withstood these challenges have not been studied. This study explores the divisional disparities in trends and disruptions in maternal health service utilisation caused by the COVID-19 pandemic. Data was extracted from the District Health Information Software of Bangladesh from January 2017 to December 2021. We assessed the trend of first antenatal care visit, institutional delivery and number of caesarean sections over these years. We explored both the yearly and monthly trends to see the variations in the number of utilisations. Segmented regression with Poisson distribution was used to assess changes in service utilisation during the COVID-19 period. We reported incidence rate ratio (IRR) of service utilisation with a 95% confidence interval (CI) in different divisions during COVID-19 (2020-2021) compared to the reference period (2017-2019). Initially, a notable decline in maternal health care utilisation was observed in 2020 compared to the pre-pandemic period of 2017-2019. Divisional disparities were observed in this trend. Overall, compared to the pre-pandemic period, we observed around 30% decline in all three selected indicators of maternal health care. The lowest value was observed in Chattogram in 2020 (IRR = 0.66; 95% CI = 0.55-0.79) and Rajshahi in 2021 (IRR = 0.71; 95% CI = 0.60-0.82). For institutional delivery, Barishal division had the lowest IRR (0.64; 95% CI = 0.60-0.68) in 2020 and, in 2021 Rajshahi had the lowest IRR (0.71; 95% CI = 0.60-0.82). For caesarean section, the lowest value was observed in Barishal division (IRR = 0.48; 95% CI = 0.44-0.53) in 2020 and in Mymensingh (IRR = 0.37; 95% CI = 0.32-0.43) in 2021. By 2021, the three maternal health care utilisation indicators demonstrated recovery. The effect of the pandemic, including lockdown, on the selected maternal service utilisation was observed in Bangladesh though there were substantial geographic disparities. These disruptions slightly recovered after the initial shock. These results will support the government in preparing the national and regional health systems for future epidemics in Bangladesh.

The coronavirus disease 2019 (COVID-19) pandemic disrupted essential health care services worldwide, including those related to immunisation. National data from Bangladesh shows that child immunisation may have been adversely affected by the pandemic but regional evidence is limited. We therefore aimed to explore the regional differences in the indirect effects of COVID-19 on child immunisation in Bangladesh. We extracted data from the District Health Information Software (DHIS2) spanning the period from January 2017 to December 2021. We examined three essential immunisation indicators: Bacille Calmette-Guérin (BCG), pentavalent third dose, and measles vaccinations. We examined both the yearly and monthly trends to explore fluctuations in the number of immunisations to pinpoint specific periods of service utilisation regression. Segmented regression with Poisson distribution was implemented given the count-based outcome. We reported incidence rate ratios (IRRs) with 95% confidence intervals (CIs) in different regions in 2020 and 2021 compared to the reference period (2017-19). We initially observed a notable decline in vaccine administration in April 2020 compared to the pre-pandemic period of 2017-19 with a drop of approximately 53% for BCG vaccines, 55% for pentavalent third doses, and 51% for measles vaccines followed by May 2020. The second half of 2020 saw an increase in vaccination numbers. There were noticeable regional disparities, with Sylhet (IRR = 0.75; 95% CI = 0.67-0.84 for pentavalent administration, IRR = 0.79; 95% CI = 0.71-0.88 for measles administration) and Chattogram (IRR = 0.77; 95% CI = 0.72-0.83 for BCG administration) experiencing the most significant reductions in 2020. In April 2020, Dhaka also experienced the largest decline of 67% in measles vaccination. In 2021, most divisions experienced a rebound in BCG and pentavalent administration, exceeding 2019 levels, except for Chittagong, where numbers continued to decline, falling below the 2019 figure. Our findings highlight the impact of the COVID-19 pandemic on childhood immunisation across regions in Bangladesh. Sylhet, Chattogram, and Dhaka divisions experienced the most significant reductions in immunisation services during 2020. This underscores the importance of targeted interventions and regional strategies to mitigate the indirect effects of future challenges on essential health care services, particularly childhood immunisation, in Bangladesh.

Background The COVID-19 pandemic posed a danger to global public health because of the unprecedented physical, mental, social, and environmental impact affecting quality of life (QoL). The study aimed to find the changes in QoL among COVID-19 recovered individuals and explore the determinants of change more than 1 year after recovery in low-resource settings. Methods COVID-19 patients from all eight divisions of Bangladesh who were confirmed positive by reverse transcription-polymerase chain reaction from June 2020 to November 2020 and who subsequently recovered were followed up twice, once immediately after recovery and again 1 year after the first follow-up. The follow-up study was conducted from November 2021 to January 2022 among 2438 individuals using the World Health Organization Quality of Life Brief Version (WHOQOL-BREF). After excluding 48 deaths, 95 were rejected to participate, 618 were inaccessible, and there were 45 cases of incomplete data. Descriptive statistics, paired-sample analyses, generalized estimating equation (GEE) analysis, and multivariable logistic regression analyses were performed to test the mean difference in participants’ QoL scores between the two interviews. Results Most participants ( n  = 1710, 70.1%) were male, and one-fourth (24.4%) were older than 46. The average physical domain score decreased significantly from baseline to follow-up, and the average scores in psychological, social, and environmental domains increased significantly at follow-up ( P  < 0.05). By the GEE equation approach, after adjusting for other factors, we found that older age groups ( P  < 0.001), being female ( P  < 0.001), having hospital admission during COVID-19 illness ( P  < 0.001), and having three or more chronic diseases ( P  < 0.001), were significantly associated with lower physical and psychological QoL scores. Higher age and female sex [adjusted odd ratio (a OR ) = 1.3, 95% confidence interval ( CI ) 1.0–1.6] were associated with reduced social domain scores on multivariable logistic regression analysis. Urban or semi-urban people were 49% less likely (a OR  = 0.5, 95% CI 0.4–0.7) and 32% less likely (a OR  = 0.7, 95% CI 0.5–0.9) to have a reduced QoL score in the psychological domain and the social domain respectively, than rural people. Higher-income people were more likely to experience a decrease in QoL scores in physical, psychological, social, and environmental domains. Married people were 1.8 times more likely (a OR  = 1.8, 95% CI 1.3–2.4) to have a decreased social QoL score. In the second interview, people admitted to hospitals during their COVID-19 infection showed a 1.3 times higher chance (a OR  = 1.3, 95% CI 1.1–1.6) of a decreased environmental QoL score. Almost 13% of participants developed one or more chronic diseases between the first and second interviews. Moreover, 7.9% suffered from reinfection by COVID-19 during this 1-year time. Conclusions The present study found that the QoL of COVID-19 recovered people improved 1 year after recovery, particularly in psychological, social, and environmental domains. However, age, sex, the severity of COVID-19, smoking habits, and comorbidities were significantly negatively associated with QoL. Events of reinfection and the emergence of chronic disease were independent determinants of the decline in QoL scores in psychological, social, and physical domains, respectively. Strong policies to prevent and minimize smoking must be implemented in Bangladesh, and we must monitor and manage chronic diseases in people who have recovered from COVID-19. Graphical Abstract

Increasing the availability and readiness of basic emergency obstetric and newborn care (BEmONC) services is essential for improving maternal and neonatal health. However, little is known about any progress made in the availability and readiness of BEmONC services in Bangladesh. Using nationally representative data from the Bangladesh Health Facility Survey conducted between 2014 and 2017, we measured changes in the availability and readiness of BEmONC services in health facilities in Bangladesh, calculating the BEmONC service availability and readiness scores according to the World Health Organization Service Availability and Readiness Assessment guideline. The percentage of health facilities performing all seven basic signal functions declined slightly from 13% in 2014 to 11% in 2017. The decline was largely noticed in Maternal and Child Welfare Centers, Upazila Health Complexes, and Union Subcenter/Rural Dispensaries, as well as in all divisions except Rangpur. No remarkable changes in overall readiness of health facilities across location, division and facility type were observed between 2014 and 2017. However, significant reductions in availability and readiness were noticed when item-specific assessment was made. Type of health facility was significantly associated with both availability and readiness scores in adjusted regression models. Appropriate strategies and efforts could improve the availability and readiness of BEmONC services in health facilities in Bangladesh.