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Sierra Leone is among the countries that recorded high under-five child mortality rate in the world. To design and implement policies that can address this public health challenge, the present study developed a predictive model of factors that explained under-five mortality in Sierra Leone using the 2008 and 2013 Sierra Leone Demographic and Health Survey (SDHS) datasets. LASSO regression technique was used to select the predictors to build the under-five predictive single-level logit and multilevel logit models. Statistical analyses were performed in the R freeware version 3.6.1. About 588 (10.4%) and 1320 (11.1%) children under five were reported dead in 2008 and 2013, respectively. The significant predictors of under-five mortality in Sierra Leone were the total number of children ever born, number of children under five in the household, mother's birth in the last five years, mother's number of living children, and number of household members, household wealth, maternal contraceptive use and intention, number of eligible women in the household, type of toilet facility, sex of the child, and weight of the child at birth. The study identified certain predictors that deserve policy attention and interventions to strengthen the efforts of creating child welfare and survival atmosphere in Sierra Leone.

Understanding direct and indirect relationships of environmental, socio-economic and climate variables and the dynamics of epidemics is key to guiding targeted public health policy and interventions. This study investigates the direct and indirect effects of environmental and socio-economic factors on the COVID-19 dynamics in Africa (54 African countries from 2019 to 2021) using SEM approach. Specifically, the study aimed to: (i) assess the performance of two SEM estimation methods (Lisrel and PLS-SEM) in relationship to sample size (100, 200, 500, and 1000) and level of model complexity (No, two, and four indirect effects) and (ii) use the most performing SEM estimation method to examine direct and indirect effects of factors influencing the number of cases and deaths of COVID-19 in Africa. The results highlight a positive spatial correlation between factors such as temperature, humidity, age, the proportion of people aged over 65, and the COVID-19 incidence. Under the control of confounding factors, Lisrel turns out to be the most performing method, identifying climate, demographic and economic factors as the main determinants of COVID-19 dynamics. These factors have a direct and significant impact on the incidence of COVID-19. An indirect relationship was also observed between economic factors and the incidence of COVID-19 through air pollutants. The results highlight the importance of considering these factors in understanding the spread of the virus to avoid further disasters. [ABSTRACT FROM AUTHOR]

Objectives: To determine the usefulness of cardiovascular physical examination (CPE) as a screening tool in a low-resource setting for detecting congenital heart disease (CHD) in newborns delivered at the Maternity Unit of Korle Bu Teaching Hospital (KBTH), Accra, Ghana., Design: A hospital-based cross-sectional study with a comparison group component., Setting: Maternity Unit of the KBTH, Accra, Ghana., Participants: Over eight months, newborns aged 1-14 days delivered at ≥ 34 weeks' gestation at the Maternity Unit, KBTH, were recruited into the study., Intervention: Each newborn was examined using a set of CPE parameters for the presence of congenital heart disease. Those with suggestive features of CHD had a confirmatory echocardiogram test., Main Outcome Measure: Abnormal CPE features and their corresponding echocardiogram findings., Results: A total of 1607 were screened, with 52 newborns showing signs of CHD on CPE, of which 20 newborns were proven on echocardiogram to have congenital heart disease. Abnormal CPE parameter that was associated with CHD was murmur (P=0.001), dysmorphism (p=0.01), newborns with chest recessions (p=0.01) and lethargy (p=0.02). CPE's sensitivity, specificity, and positive and negative predictive values were 95%, 60.7%, 36.5% and 98,1%, respectively. The most common acyanotic CHD found was isolated atrial septal defect (ASD), followed by patent ductus arteriosus (PDA). The only cyanotic CHD found was a case of tricuspid atresia., Conclusion: Cardiovascular physical examination at birth is an effective and inexpensive screening tool for detecting CHD in newborns, which can easily be utilised in low-resource settings., Funding: None declared., Competing Interests: Conflict of interest: None declared, (Copyright © The Author(s).)

Ghana might not meet the SDGs target 3.2 of reducing neonatal mortality to 12 deaths per 1000 live births by 2030. Identifying core determinants of neonatal deaths provide policy guidelines and a framework aimed at mitigating the effect of neonatal deaths. Most studies have identified household and individual-level factors that contribute to neonatal mortality. However, there are relatively few studies that have rigorously assessed geospatial covariates and spatiotemporal variations of neonatal deaths in Ghana. This study focuses on modeling and mapping of spatiotemporal variations in the risk of neonatal mortality in Ghana using Bayesian Hierarchical Spatiotemporal models. This study used data from the Ghana Demographic and Health Surveys (GDHS) conducted in 1993, 1998, 2003, 2008, and 2014. We employed Bayesian Hierarchical Spatiotemporal regression models to identify geospatial correlates and spatiotemporal variations in the risk of neonatal mortality. The estimated weighted crude neonatal mortality rate for the period under consideration was 33.2 neonatal deaths per 1000 live births. The results obtained from Moran's I statistics and CAR model showed the existence of spatial clustering of neonatal mortality. The map of smooth relative risk identified Ashanti region as the most consistent hot-spot region for the entire period under consideration. Small body size babies contributed significantly to an increased risk of neonatal mortality at the regional level [Posterior Mean: 0.003 (95% CrI: 0.00,0.01)]. Hot spot GDHS clusters exhibiting high risk of neonatal mortality were identified by LISA cluster map. Rural residents, small body size babies, parity, and aridity contributed significantly to a higher risk of neonatal mortality at the GDHS cluster level. The findings provide actionable and insightful information to prioritize and distribute the scarce health resources equitably to tackle the menace of neonatal mortality. The regions and GDHS clusters with excess risk of neonatal mortality should receive optimum attention and interventions to reduce the neonatal mortality rate., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Kwami Takramah 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.)