Unveiling the dimension of regional disparities: Assessing the disruption of immunisation services by COVID-19 in Bangladesh.

Background: 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. Methods: 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). Results: 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. Conclusions: 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.

Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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%). CONCLUSIONS: 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.

Introducing a standardised register for strengthening the inpatient management of newborns and sick children: Implementation research in selected health facilities of Bangladesh.

Background: It is imperative to maintain accurate documentation of clinical interventions aimed at enhancing the quality of care for newborns and sick children. The National Newborn Health and IMCI programme of Bangladesh led the development of a standardised register for managing newborns and sick children under five years of age during inpatient care through stakeholder engagement. We aimed to assess the implementation outcomes of the standardised register in the inpatient department. Methods: We conducted implementation research in two district hospitals and two sub-district hospitals of Kushtia and Dinajpur districts from November 2022 to January 2023 to assess the implementation outcomes of the standardised register. We assessed the following World Health Organization implementation outcome variables: usability, acceptability, adoption (actual use), fidelity (completeness and accuracy), and utility (quality of care) of the register against preset benchmarks. We collected data through structured interviews with health care providers; participant enrolment; and data extraction from inpatient registers and case record forms. Results: The average usability and acceptability scores among health care providers were 73 (standard deviation (SD) = 14) and 82 (SD = 14) out of 100, respectively. The inpatient register recorded 96% (95% confidence interval (CI) = 95-97) of under-five children who were admitted to the inpatient department (adoption - actual use). The proportions of completed data elements in the inpatient register were above the preset benchmark of 70% for all the assessed data elements except 'investigation done' (24%; 95% CI = 23-26) (fidelity - completeness). The percentage agreements between government-appointed nurses posted and study-appointed nurses were above the preset benchmark of 70% for all the reported variables (fidelity - accuracy). The kappa coefficient for the overall level of agreement between these two groups regarding reported variables indicated moderate to substantial agreement. The proportion of newborns with sepsis receiving injectable antibiotics was 62% (95% CI = 47-75) (utility - quality of care). We observed some variability in the completeness and accuracy of the inpatient register by district and facility type. Conclusions: The inpatient register was positively received by health care providers, with evaluations of implementation outcome variables showing encouraging results. Our findings could inform evidence-based decision-making on the implementation and scale-up of the inpatient register in Bangladesh, as well as other low- and middle-income countries.

Factors influencing delivery-related complications and their consequences in hard-to-reach areas of Bangladesh.

BACKGROUND AND OBJECTIVES: Bangladesh's high maternal mortality ratio is exacerbated by delivery-related complications, particularly in hard-to-reach (HtR) areas with limited healthcare access. Despite this, few studies have explored delivery-related complications and factors contributing to these complications among the disadvantaged population. This study aimed to investigate the factors contributing to delivery-related complications and their consequences among the mothers residing in the HtR areas of Bangladesh. METHODS: Data were collected using a cross-sectional study design from 13 HtR sub-districts of Bangladesh between September 2019 and October 2019. Data from 1,290 recently delivered mothers were analysed. RESULTS: Around 32% (95% CI: 29.7-34.8) of the mothers reported at least one delivery-related complication. Prolonged labour pain (21%) was the highest reported complication during the delivery, followed by obstructive labour (20%), fever (14%), severe headache (14%). Mothers with higher education, a higher number of antenatal care (ANC) visits, complications during ANC, employed, and first-time mothers had higher odds of reporting delivery-related complications. More than one-half (51%) of these mothers had normal vaginal delivery. Nearly one-fifth (20%) of mothers who reported delivery-related complications were delivered by unskilled health workers at homes. On the other hand, about one-fifth (19%) of the mothers without any complications during delivery had a caesarean delivery. Nine out of ten of these caesarean deliveries were done at the private facilities. CONCLUSION: Delivery-related complications are significantly related to a woman's reproductive history and other background characteristics. Unnecessary caesarean delivery is prominent at private facilities.

Insomnia and job stressors among healthcare workers who served COVID-19 patients in 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.