Background: The rapid growth in cases of COVID-19 has challenged national healthcare capacity, testing systems at an advanced ICU, and public health infrastructure level. This global study evaluates the association between multi-factorial healthcare capacity and case fatality of COVID-19 patients by adjusting for demographic, health expenditure, population density, and prior burden of non-communicable disease. It also explores the impact of government relationships with civil society as a predictor of infection and mortality rates. Methods: Data were extracted from the Johns Hopkins University database, World Bank records and the National Civic Space Ratings 2020 database. This study used data from 86 countries which had at least 1,000 confirmed cases on 30th April 2020. Negative binomial regression model was used to assess the association between case fatality (a ratio of total number of confirmed deaths to total number of confirmed cases) and healthcare capacity index adjusting for other covariates. Findings: Regression analysis shows that greater healthcare capacity was related to lesser case-fatality [incidence rate ratio (IRR) 0.5811; 95% confidence interval (CI) 0.4727–0.7184; p < 0.001] with every additional unit increase in the healthcare capacity index associated with a 42% decrease in the case fatality. Health expenditure and civil society variables did not reach statistical significance but were positively associated with case fatalities. Interpretation: Based on preliminary data, this research suggests that building effective multidimensional healthcare capacity is the most promising means to mitigate future case fatalities. The data also suggests that government's ability to implement public health measures to a degree determines mortality outcomes.

Low birth weight (LBW) remains a leading global cause of childhood morbidity and mortality. This study leverages a large national survey to determine current prevalence and socioeconomic, demographic and heath related factors associated with LBW in Bangladesh. Data from the Multiple Indicator Cluster Survey (MICS) 2012–13 of Bangladesh were analyzed. A total of 2319 women for whom contemporaneous birth weight data was available and who had a live birth in the two years preceding the survey were sampled for this study. However, this analysis only was able to take advantage of 29% of the total sample with 71% missing birth weight for newborns. The indicator, LBW (< 2500 g) of infants, was examined as the outcome variable in association with different socioeconomic, demographic and health-related covariates. Mixed-effects logistic regression was performed to identify possible factors related to LBW. In the selected sub-sample, about 20% of infants were born with LBW, with lowest rates observed in Rajshahi (11%) and highest rates in Rangpur (28%). Education of mothers (adjusted odds ratio [AOR] 0.52, 95% confidence interval [CI] 0.39–0.68 for secondary or higher educated mother) and poor antenatal care (ANC) (AOR 1.40, 95% CI 1.04–1.90) were associated with LBW after adjusting for mother’s age, parity and cluster effects. Mothers from wealthier families were less likely to give birth to an LBW infant. Further indicators that wealth continues to play a role in LBW were that place of delivery, ANC and delivery assistance by quality health workers were significantly associated with LBW. However there has been a notable fall in LBW prevalence in Bangladesh since the last comparable survey (prevalence 36%), and an evidence of possible elimination of rural/urban disparities. Low birth weight remains associated with key indicators not just of maternal poverty (notably adequate maternal education) but also markers of structural poverty in health care (notably quality ANC). Results based on this sub-sample indicate LBW is still a public health concern in Bangladesh and an integrated effort from all stakeholders should be continued and interventions based on the study findings should be devised to further reduce the risk of LBW.

Following detection of the first few COVID-19 cases in early March, Bangladesh has stepped up its efforts to strengthen capacity of the healthcare system to avert a crisis in the event of a surge in the number of cases. This paper sheds light on the preparedness of the healthcare system by examining the spatial distribution of isolation beds across districts and divisions, forecasting the number of ICU units that may be required in the short term and analyzing the availability of frontline healthcare workers to combat the pandemic. As of May 2, COVID-19 cases have been found in 61 of the 64 districts in Bangladesh with Dhaka District being the epicenter. Seventy-one percent of the cases have been identified in 6 neighboring districts, namely, Dhaka, Narayanganj, Gazipur, Narsingdi, Munsiganj and Kishoreganj, which appear to form a spatial cluster. However, if one takes into account the population at risk, the prevalence appears to be highest in Dhaka, followed by Narayanganj, Gazipur, Kishorganj, Narsingdi and Munshiganj. These regions may therefore be flagged as the COVID-19 hotspots in Bangladesh. Among the eight divisions, prevalence is highest in Dhaka Division followed by Mymensingh. The number of cases per million exceeds the number of available isolation beds per million in the major hotspots indicating that there is a risk of the healthcare system becoming overwhelmed should the number of cases rise. This is especially true for Dhaka Division, where the ratio of COVID-19 patients to doctors appears to be alarmingly high. My-mensingh Division also has a disproportionately small number of doctors relative to the number of COVID-19 patients. Using second order polynomial regression, the analysis predicts that even if all ICU beds are allocated to COVID-19 patients, Bangladesh may run out of ICU beds soon after May 15, 2020. We conclude that in spite of a significant increase in hospital capacity during 2005-15 and a 57 % rise in the number of doctors during the same period, the healthcare system in Bangladesh and Dhaka Division in particular, may not be fully prepared to handle the COVID-19 crisis. Thus, further steps need to be taken to flatten the curve and improve healthcare capacity.