Global variation of COVID-19 mortality rates in the initial phase

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.

Global variation of COVID-19 mortality rates in the initial phase

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.

Factors associated with low birthweight in term pregnancies: a matched case–control study from rural Pakistan

Low birthweight [LBW] remains a significant public health problem in Pakistan and further understanding of factors associated with LBW is required. We conducted a hospital-based matched case control study to identify risk factors associated with LBW in a rural district of Pakistan. We found that illiteracy [AOR: 2.68; 95% CI: 1.59 - 4.38], nulliparity [AOR: 1.82; 95% CI: 1.26-2.44], having a previous miscarriage/abortion [AOR: 1.22; 95% CI: 1.06-2.35], having < 2 antenatal care [ANC] visits during last pregnancy [AOR: 2.43; 95% CI: 1.34-2.88], seeking ANC in third trimester [AOR: 3.62; 95% CI: 2.14-5.03], non-use of iron folic acid during last pregnancy [AOR: 2.72; 95% CI: 1.75-3.17], having hypertension during last pregnancy [AOR: 1.42; 95% CI: 1.13-2.20], being anemic [AOR: 2.67; 95% CI: 1.65-5.24] and having postpartum weight of <45 kg [AOR: 3.30; 95% CI: 1.97-4.52] were significantly associated with an increased odds of having a LBW baby. Our study identifies modifiable risk factors requiring immediate commitment from the health authorities