COVID-19 Deaths: Which Explanatory Variables Matter the Most?

SO_SCPLOWUMMARYC_SCPLOWAs Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spreads around the World, many questions about the disease are being answered; however, many more remain poorly understood. Although the situation is rapidly evolving, with datasets being continually corrected or updated, it is crucial to understand what factors may be driving transmission through different populations. While studies are beginning to highlight specific parameters that may be playing a role, few have attempted to thoroughly estimate the relative importance of these disparate variables that likely include: climate, population demographics, and imposed state interventions. In this report, we compiled a database of more than 28 potentially explanatory variables for each of the 50 U.S. states through early May 2020. Using a combination of traditional statistical and modern machine learning approaches, we identified those variables that were the most statistically significant, and, those that were the most important. These variables were chosen to be fiduciaries of a range of possible drivers for COVID-19 deaths in the USA. We found that population-weighted density (PWD), some "stay at home" metrics, monthly temperature and precipitation, race/ethnicity, and chronic low-respiratory death rate, were all statistically significant. Of these, PWD and mobility metrics dominated. This suggests that the biggest impact on COVID-19 deaths was, at least initially, a function of where you lived, and not what you did. However, clearly, increasing social distancing has the net effect of (at least temporarily) reducing the effective PWD. Our results strongly support the idea that the loosening of "lock-down" orders should be tailored to the local PWD. In contrast to these variables, while still statistically significant, race/ethnicity, health, and climate effects could only account for a few percent of the variability in deaths. Where associations were anticipated but were not found, we discuss how limitations in the parameters chosen may mask a contribution that might otherwise be present.

Consumption of Cow's Milk in Early Childhood and Fracture Risk: A Prospective Cohort Study.

Cow's milk is consumed by most North American children, yet the relationships between the volume and fat content of cow's milk consumed and childhood fracture risk are unclear. Our primary objectives in this study were to evaluate whether the volume or fat content of cow's milk consumed at 1-3 years of age was associated with the risk of fracture between 3 and 10 years of age. This was a prospective cohort study of 2,466 children enrolled in Toronto, Ontario, Canada, between 2008 and 2016. The primary exposure was volume of cow's milk consumed between the ages of 1 and 3 years, and the secondary exposure was average percentage of milk fat consumed by each child during the same period. The primary outcome was a parental report of child fracture at ages 3-10 years. In the primary and secondary adjusted analyses, no association between milk volume and fracture risk (adjusted relative risk = 1.04, 95% confidence interval: 0.87, 1.26) or between milk-fat content and fracture risk (adjusted relative risk = 1.05, 95% confidence interval: 0.84, 1.31) was observed. In this study, we did not identify a protective association of early childhood cow's milk volume or fat consumption with fracture risk in later childhood. Future prospective research is needed to understand whether cow's milk is beneficial for fracture prevention through the life course.