In response to your peers, state whether their interpretation makes sense to you or what additional questions you have and discuss what other data and statistical analysis would hel

Jeff Discussion:

Bakaloudi and Chourdakis (2022), examined the possible relationship between vitamin D deficiency and COVID-19 prevalence in Europe. With international concern for COVID-19 infections, many possible prophylactic treatment regimens have been advertised in the media. There is a need for scientific examination of these treatments to ensure the public is not misled into a false sense of security. The study variables were the percentage of vitamin D deficient by country (x-axis) and COVID-19 infections per one million population (y-axis). Two scatterplots were created, one defined vitamin D deficiency as less than 50 nmol/L. The second sought to examine severe vitamin D deficiency as defined by levels less than 30 nmol/L. Both scatter plots showed positive, weak, non-statistically significant correlations (r=0.363, p=0.166; r=0.215, p=0.392). 

Next the study examined vitamin D deficiency and association with COVID-19 mortality. The same parameters for vitamin D deficiency and severe vitamin D deficiency were used and two more scatterplots were created. In these tests, vitamin D deficiency showed a moderate, positive, statistically significant associated with mortality from COVID-19 (r = 0.634, p = 0.003). The severe vitamin D deficient group had similar findings (r = 0.538, p = 0.021). 

Some information needed to clarify these findings may include the seasons in that the data was collected, as sun exposure affects vitamin D synthesis. Additionally, the study does not examine how preexisting conditions that contribute to COVID-19 mortality may also contribute to vitamin D deficiency. Despite a moderate correlation in the mortality cases, it is important to not jump to conclusions that vitamin D can lessen the chance of dying from COVID-19. This is especially true because the study does not examine if supplementing vitamin D is beneficial and only examines COVID-19 in the setting of preexisting vitamin D deficiency.  

References 

Bakaloudi, D. R., & Chourdakis, M. (2022). A critical update on the role of mild and serious vitamin D deficiency prevalence and the COVID-19 epidemic in Europe. Nutrition, 93.  https://doi-org.ezproxy.snhu.edu/10.1016/j.nut.2021.111441 

Claudia Discussion:

Ortiz et al. (2011) explore the integrity of Google Flu Trends estimates of US influenza-like illnesses (ILI) and laboratory-confirmed influenza infections. Google Flu Trends was developed due to the 2009 H1N1 outbreak to aid in the surveillance of ILI through internet searches. During 2007-2008, Google Flu Trends estimates were highly correlated to CDC ILI surveillance, with a correlation coefficient of 0.97 (Ortiz et al., 2011). In the study, Google Trends ILI has been compared against data from the Centers for Disease and Prevention (CDC) Virus Surveillance. The scatterplot visualizes the correlation between Google Flu Trends and CDC ILI Surveillance data from 2003 to 2008. Pearson’s correlation coefficient describing the strength of association between Google Flu Trends with CDC Virus Surveillance is 0.72 (Ortiz et al., 2011). A second comparison was performed between CDC ILI Surveillance and CDC Virus Surveillance over the same period, resulting in a Pearson’s correlation coefficient of 0.85 (Ortiz et al., 2011). This information tells us that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance of laboratory-confirmed influenza. CDC ILI surveillance was more closely correlated with CDC Virus Surveillance. Further information that may be needed includes convenience sample characteristics, health-seeking behaviors, testing practices, demographics, and location (Ortiz et al., 2011). This information may impact the interpretation of the data correlation. While Google Flu Trends has the potential to become a useful public health tool, there is more refinement needed for its statistical model.

Ortiz, J. R., Zhou, H., Shay, D. K., Neuzil, K. M., Fowlkes, A. L., & Goss, C. H. (2011). Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google Flu Trends.  PloS one,  6(4), e18687.  https://doi.org/10.1371/journal.pone.0018687

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