ABSTRACT
Objectives@#Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and lessaffected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden. @*Methods@#We identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/ states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation– maximization) clustering algorithm. @*Results@#Statistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p < 0.05) @*Conclusion@#Countries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis.
ABSTRACT
Objectives@#Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and lessaffected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden. @*Methods@#We identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/ states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation– maximization) clustering algorithm. @*Results@#Statistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p < 0.05) @*Conclusion@#Countries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis.
ABSTRACT
The study analyzed the demographic and socio-economic determinants of neonatal mortality. The variables included one fetal variable [gender], three maternal variables [level of education, occupation, age], three paternal variables [level of education, occupation, age], and seven household [family] variables [nationality, consanguinity, family income, house ownership, type of housing, family type, domestic help]. One calendar year data [January to December 2011] was extracted from Qatar's National Perinatal Registry and analyzed using a univariate regression model. Qatar had a total of 20,583 live births and 102 neonatal deaths during 2011 [NMR 4.95/1000]. Less than secondary school maternal education level, as compared to secondary school or above maternal education level, was the only variable significantly associated with neonatal mortality [OR 2.08, 95% CI 1.23 - 3.53, p=0.009]. The association between the remaining thirteen variables and neonatal mortality was non-significant. Priority investment to raise female literacy above secondary school level may significantly improve neonatal survival
ABSTRACT
PEARL Study [Perinatal Neonatal Outcomes Research Study in the Arabian Gulf] is Qatar's prospective National Perinatal Epidemiologic Study funded by QNRF [Qatar National Research Fund - grant # QNRF-NPRP-09-390-3-097]. The study is a joint collaborative research project between Hamad Medical Corporation [HMC], Doha, Qatar and the University of Gloucestershire, Gloucester, United Kingdom. The project aims at building a National Neonatal Perinatal Registry for Qatar called Q-Peri-Reg which will be used to quantify Maternal, Neonatal and Perinatal mortality and morbidities and their correlates. The study is approved by the Research Ethics Committee [IRB] of Hamad Medical Corporation [protocol#9211/09], which is responsible for providing ethical approval to all health care research projects in the State of Qatar