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Background: Different adaptation and changes have been practiced during COVID-19 pandemic. Objective: In this paper we investigated the impact of the COVID-19 pandemic on service delivery in the department of interventional neurology and adaptation of the department to the changed environment. Methodology: This was a retrospective analysis of diagnostic digital subtraction angiography (DSA) procedures done from January 2018 to December 2020 and were analyzed to detect significant breaks in time trend. Results: A total of 358, 426 and 251 patients got admitted for DSA in consecutive three years from 2018 to 2020 respectively. There was a sudden drop in the number of DSA procedures from 30 to 50 patients per month in the pre-COVID era before March 2020 to less than 10 patients per month during the COVID period of March to June 2020. However, the situation gradually improved following the introduction of RT-PCR test for SARS CoV-2 in June 2020. A poison regression showed a significant increase in monthly DSA procedures in the year 2019 compared to the year 2018, but a significant decrease in the pandemic year of 2020. Conclusion: By incorporating COVID-19 testing as a pre-requisite test before DSA procedures, the department of interventional neurology recovered from experiencing a significant drop in the number of performed DSA procedures in the initial periods of the pandemic to reaching a level observed in the pre-COVID era. Bangladesh Journal of Infectious Diseases, April 2022;9(Suppl_1): S3-S8

Early childhood development (ECD) is a crucial determinant of adulthood capabilities. This paper investigates the current ECD status among young children of 3–4 years of age in Bangladesh and its r...

A major feature of health-care systems is substantial variation in hospital productivity. Hospital productivity varies widely across countries. The presence of such variation suggests potential areas for improvement, which can substantially lower health-care costs. This research aims to investigate factors that may explain variations in hospital productivity by constructing a longitudinal data (panel) on English NHS hospital trusts. It also seeks to explore possible interactions among the factors in a data-driven manner. We employ unbiased panel regression tree techniques from the machine-learning literature to explore the complex interactive structure of the data. We next use econometric panel regression to deal with individual hospital effects to identify some of the determinants of hospital productivity. The findings point to the significance of efficiency-enhancing mechanisms for hospital productivity, including measures to reduce the length of stay, increase day case (outpatient) surgery rate, and to minimize errors. Further, such measures are shaped by more fundamental factors such as the availability of human capital and management practices. Our results underscore the importance of within-hospital efficiency-enhancing mechanisms to cost-adjusted hospital productivity. Improving hospital operational processes will enhance productivity. At a deeper level, human capital and management practices are likely to be most critical.

BackgroundFatigue has been observed after a number of infectious disease outbreaks around the world. After the outbreak of SARS CoV-2 in Wuhan, China in 2019, the disease turned into a pandemic very rapidly. Mental health is a key issue associated with such outbreaks. To explore the fatigue level among physicians working in designated public and private hospitals in Bangladesh, we conducted a matched case-control study of post-SARS-CoV-2 fatigue.MethodIn this study 105 physicians who were diagnosed as COVID-19 infected, got treatment, and declared cured at least 6 weeks before the interview date, were recruited as cases and the same number of age and designation matched healthy physicians as control who are working in the same hospital. Case and control were selected in 1:1 ratio from each of the hospitals. The study population was selected by inclusion and exclusion criteria after taking informed written consent. Data collection was done by a semi-structured questionnaire. Diagnosis of COVID--19 infection was done by detection of SARS CoV-2 antigen by RT-PCR from reference laboratories in Bangladesh or by HRCT Chest.ResultAround two-thirds of the physicians were male (67.6% versus 32.4%). Most of them aged less than forty years (80.5%). The cases had a greater number of comorbid conditions than those who were negative. The FSS score (mean) was much higher for cases (36.7 ± 5.3 versus 19.3 ± 3.8) than the control group with a statistically significant difference with no significant gender differentiation. Similarly, around 67.7% of the previously COVID positive physicians represented in the highest FSS score tertile compared to the respondents in the control group had a mean score of less than 3. The difference was also highly significant.ConclusionPhysicians, who had a previous history of COVID-19 infection had a higher total and mean FSS score, signifying a more severe level of fatigue than the physicians who had never been COVID-19 positive while working in the same hospital irrespective of their age and sex.

This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014 and pertain to a sample of 6,170 under-5 children. Social and economic determinants such as wealth, mother’s decision making on healthcare, parental education are considered in addition to geographic divisions and common demographic characteristics of children including age, sex and birth order. A classification tree was first constructed to identify important interaction-based rules that characterize children with different profiles of risk for stunting. Then binary logistic regression models were fitted to measure the importance of these interactions along with the individual risk factors. Results revealed that, as individual factors, living in Sylhet division (OR: 1.57; CI: 1.26–1.96), being an urban resident (OR: 1.28; CI: 1.03–1.96) and having working mothers (OR: 1.21; CI: 1.02–1.44) were associated with higher likelihoods of childhood stunting, whereas belonging to the richest households (OR: 0.56; CI: 0.35–0.90), higher BMI of mothers (OR: 0.68 CI: 0.56–0.84) and mothers’ involvement in decision making about children’s healthcare with father (OR: 0.83, CI: 0.71–0.97) were linked to lower likelihoods of stunting. Importantly however, risk classifications defined by the interplay of multiple sociodemographic factors showed more extreme odds ratios (OR) of stunting than single factor ORs. For example, children aged 14 months or above who belong to poor wealth class, have lowly educated fathers and reside in either Dhaka, Barisal, Chittagong or Sylhet division are the most vulnerable to stunting (OR: 2.52, CI: 1.85–3.44). The findings endorse the need for tailored-intervention programs for children based on their distinct risk profiles and sociodemographic characteristics.

BackgroundWith the proposed pathophysiologic mechanism of neurologic injury by SARS COV-2 the frequency of stroke and henceforth the related hospital admissions were expected to rise. In this paper we investigate this presumption by comparing the frequency of admissions of stroke cases in Bangladesh before and during the pandemic.MethodsWe conducted a retrospective analysis of stroke admissions in a 100-bed stroke unit at the National Institute of Neurosciences and Hospital (NINS&H) which is considerably a large stroke unit. We considered all the admitted cases from the 1st January to the 30th June, 2020. We used Poisson regressions to determine whether statistically significant changes in admission counts can be found before and after 25 March since when there is a surge in COVID-19 infections.ResultsA total of 1394 stroke patients got admitted during the study period. Half of the patients were older than 60 years, whereas only 2.6% were 30 years old or younger with a male-female ratio of 1.06:1. From January to March, 2020 the mean rate of admission was 302.3 cases per month which dropped to 162.3 cases per month from April to June with an overall reduction of 46.3% in acute stroke admission per month. In those two periods, reductions in average admission per month for ischemic stroke (IST), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH) and venous stroke (VS) were 45.5%, 37.2%, 71.4% and 39.0%, respectively. Based on weekly data, results of Poisson regressions confirm that the average number of admissions per week dropped significantly during the last three months of the sample period. Further, in the first three months, a total of 22 cases of hyperacute stroke management were done whereas in the last three months there was an 86.4% reduction in the number of hyperacute stroke patients getting reperfusion treatment. Only 38 patients (2.7%) were later found to be RT- PCR for SARS Cov-2 positive based on nasal swab testing.ConclusionOur study revealed more than fifty percent reduction in acute stroke admission during the COVID-19 pandemic. It is still elusive whether the reduction is related to the fear of getting infected by COVID-19 from hospitalization or the overall restriction on public movement and stay-home measures.

Summary This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.

Background Violence against children has been a persistent problem in developing nations. The adverse effects of physical violence bear a considerable impact on children’s physical and psychological development resulting in both short and long-term issues. Objective The aim of this study was to explore whether children with cognitive and social-emotional difficulties (CSEDs) were at a higher risk of experiencing physical abuse and whether mothers’ views on intimate partner violence (IPV) were also related to physical abuse against children. Participants and Setting The Bangladesh Multiple Indicator Cluster Survey-2019 was used with a sample of 27,086 children aged 5–14. Methods Generalized linear modelling along with a machine learning method of classification trees was employed to investigate the important sociodemographic characteristics and identify the most vulnerable groups of children based on their likelihood of exposure to household-violence. Results Nearly 62.5 % of the children were physically abused by their mothers. Children with CSEDs were 53 % (OR 1.53; 95 % CI: 1.41, 1.67) more likely to experience physical abuse and mothers’ justification of IPV was associated with a 16 % higher risk (OR 1.16; 95 % CI: 1.08, 1.26). Moreover, younger children aged 11 or below belonged to the high-risk groups of experiencing abuse. Conclusions The findings suggest that violence against children is widespread in Bangladesh, especially in children having CSEDs. Mothers’ acceptance of IPV was also associated with increased abusive practice against children. Sincere focus on these issues is imperative if Bangladesh intends to achieve the sustainable development goal 16.2 of eradicating all forms of violence against children and ensure their safe development.

Substantial research has been carried out to explain the effects of economic variables on obesity, typically considering only a few factors at a time, using parametric linear regression models. Recent studies have made a significant contribution by examining economic factors affecting body weight using the Behavioral Risk Factor Surveillance System data with 27 state-level variables for a period of 20 years (1990-2010). As elsewhere, the authors solely focus on individual effects of potential drivers of obesity than critical interactions among the drivers. We take some steps to extend the literature and gain a deeper understanding of the drivers of obesity. We employ state-of-the-art data mining techniques to uncover critical interactions that may exist among drivers of obesity in a data-driven manner. The state-of-the-art techniques reveal several complex interactions among economic and behavioral factors that contribute to the rise of obesity. Lower levels of obesity, measured by a body mass index (BMI), belong to female individuals who exercise outside work, enjoy higher levels of education and drink less alcohol. The highest level of obesity, in contrast, belongs to those who fail to exercise outside work, smoke regularly, consume more alcohol and come from lower income groups. These and other complementary results suggest that it is the joint complex interactions among various behavioral and economic factors that gives rise to obesity or lowers it; it is not simply the presence or absence of individual factors.

A major feature of health care systems is substantial variation in health care quality across hospitals. The quality of stroke care widely varies across NHS hospitals. We investigate factors that may explain variations in health care quality using measures of quality of stroke care. We combine NHS trust data from the National Sentinel Stroke Audit with other data sets from the Office for National Statistics, NHS and census data to capture hospitals’ human and physical assets and organisational characteristics. We employ a class of non-parametric methods to explore the complex structure of the data and a set of correlated random effects models to identify key determinants of the quality of stroke care. The organisational quality of the process of stroke care appears as a fundamental driver of clinical quality of stroke care. There are rich complementarities amongst drivers of quality of stroke care. The findings strengthen previous research on managerial and organisational determinants of health care quality.

A major feature of healthcare systems is substantial variation in healthcare quality across hospitals. The quality of stroke care widely varies across NHS hospitals. We investigate factors that may explain variations in healthcare quality using measures of quality of stroke care. We combine NHS trust data from National Sentinel Stroke Audit with other datasets from the Office for National Statistics, NHS and census data to capture hospitals' organisational, structural and regional characteristics. We employ a class of non-parametric methods to explore the complex structure of the data and a set of correlated random effects models to identify key determinants of the quality of stroke care. The organisational quality of the process of stroke care appears as a fundamental driver of clinical quality of stroke care. There are rich complementarities amongst drivers of quality of stroke care. The findings strengthen previous research on managerial and organisational determinants of healthcare quality.

This study aimed to investigate relationships between near-home street patterns and children’s time spent outdoors (TSO). Participants were 60 (n = 60) school-age Dhaka children, 7–11 years old (16 girls and 44 boys) selected by a two-phase cluster sampling method. Data were collected from September 2010 to June 2011 by visiting each of 60 children’s homes. Children’s mean TSOs (in minutes) were reported by parents’ face-to-face interviews, and near-home street pattern data were collected by systematic direct observations. The researchers also collected data on seven socio-demographic variables and three neighborhood built-environment variables. A backward selection based multiple linear regression was used to examine association between Children’s TSO and near-home street patterns. Results (adjusted R2 = 0.66 for weekdays and 0.68 for weekend) suggested that children’s TSO were significantly associated with near-home street type: dead-end instead of through streets (28 minutes on weekdays, p < .01 and 66 minutes on weekend, p < .01). The width of the street, level of its branching and availability of an open space or playground near the house are also positively associated with TSO. Near-home street features significantly contribute to TSO in school-going children of Dhaka.

Forecasting strategies that are robust to structural breaks have earned renewed attention in the literature. They are built on weighted averages downweighting past information and include forecasting with rolling window, exponential smoothing or exponentially weighted moving average and forecast pooling. These simple strategies are particularly attractive because they are easy to implement, possibly robust to different types of structural change and can adjust for breaks in real time. This review introduces the dynamic model to be forecast, explains in detail how the data-dependent tuning parameter for discounting the past data is selected and how basic forecasts are constructed and the forecast error estimated. It comments on the forecast error and the impact of weak and strong dependence of noise on the quality of the prediction. It also describes various forecasting methods and evaluates their practical performance in robust forecasting.