SNHU public Health Statistics Discussion

identify a peer-reviewed public health journal article or data results from the CDC or other public health government websites that include a scatterplot and/or correlation statistic measuring the association between two continuous variables. Define the two continuous variables and describe the association between them using the scatterplot and/or correlation statistic. What information is this scatterplot and/or correlation statistic telling us about the public health issue? What other information might you need to clarify the association between these two variables and the public health issue?

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 help clarify the association and public health issue.


At first glance, I though this study was comparing various allergens to asthma incidence but it assesses the efficacy of residents’ ability to effectively self-collect dust samples for home evaluation of allergens and mold compared to samples collected by an industrial hygienist (IH) in relatively low humidity levels in Colorado.  The Institute of Medicine (IOM) previously reported sufficient evidence of a causal relationship (not just correlation) between exposure to the four main allergens (dog, cat, dust mite and cockroach) and asthma exacerbation (IOM, 2000).  Lack of a standardized measurement makes fungal assessment more difficult to quantify but the Relative Moldiness Index (RMI) provides a numeric estimate ranging from -10 to 20 (Van Dyke et al., 2012).The study used paired t-tests to compare the occupant- and IH-collected samples.  Independent t-tests were used to determine allergen concentrations and RMIs by various factors, such as pet, housing, or water damage characteristics.  The correlation between the numbers of dogs or cats and associated allergen levels were determined by Pearson correlations and linear regression was used to evaluate the level of agreement of allergen concentrations between the occupant- and IH-collected specimens.  The presence of any dog was linked to a significantly higher (p < 0.0001) level of dog allergen, regardless of how many.  There was also a strong correlation (r2 = 0.92) between the two sample collection types according to Pearson’s correlation coefficient (Gerstman, 2014).  Cat allergen concentrations yielded similar results (r = 0.81 for resident- and r = 0.83 for IH-collected samples).  Both cat and dog allergens resulted in positive, strong, linear correlations (figures a. & b.).  Dust mite and cockroach allergens still resulted in positive, yet weaker linear correlations, as seen in figures c. & d.  The mold samples yielded no significant difference pairwise and a fairly strong, linear relationship (r2= 0.68) as seen in figure e.  This study found that residents can effectively collect dust samples as a screening tool to determine if further evaluation is required for allergens and fungi related to asthma exacerbation (Van Dyke et al., 2012).In the chart below a study was done on Covid-19 mortality and the relationship it had with Vitamin D deficiency.  The study was done around the world in various countries.  In the table below we see that in majority of the countries people who died of Covid-19 were around 45% to 60% deficient. With a large number of the population.  You do see Finland and Norway with the least number of deaths, which in turn leads to the lowest vitamin D deficiency.  Other factors that may play a part in this is the environment. Were they talking vitamins beforehand, what foods were given or were they eating? As well as other factors. What made this a study and how will this be used to help the fight against Covid-19? As mention in the article : "State-level parameters include diverse factors, such as a country's preparedness, actions of the governments, health infrastructure, timing of lockdowns, rapid border closures, implementation of social distancing, and socioeconomic status [3], whereas the individual level includes sociodemographic factors and other determinants of health status, such as sex, age, chronic diseases, obesity, and malnutrition " (Bakaloudi, D. R., & Chourdakis, M. (2022)

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