506 Assignment 07 CT

Heart Study Data Report

Introduction

The prevalence of adults and children to have a high body mass index or obesity has increased considerably since the 1970s. In general, body mass index is a cheap and easy method of determining weight category, for example, weight, obesity, overweight and normal or healthy weight. Some studies have shown that the higher the body mass index, the more the risk of cardiovascular diseases like diabetes, high blood pressure, and metabolic diseases. The purpose of this report is to analyze the findings of how body mass index is related to age, gender, diabetes, smoking status, pressure and systolic blood.

Discussions

According to Hales, Carroll, Fryar and Ogden (2017) body mass index has relationship with the gender of people. This is in line with the Framingham heart findings. From the data, the null hypothesis for sex states that H0 BMI is not related to the sex of a patient. Contrastingly, the alternative hypothesis states that H1 BMI is related to the gender of a patient. From the results, it can be concluded that the difference between BMI is statistically significant as calculated by the one-way ANOVA (F =3.3633, p=0.045). Since the p-value is less that the critical value then, the null hypothesis should be rejected. Female gender end to have a higher BMI that males.

Table 1. One -way ANOVA results of sex.

Table 2: One -way ANOVA results for diabetes

Regarding current smoking status, people who have this habit tend to have low BMI. From the Framingham heart study data, the null hypothesis for current smoking status states that H0 BMI is not related to the current smoking status of a patient. On the other hand, the alternative hypothesis states that H1 BMI is related to the current smoking status of a patient. From the results, it can be concluded that the difference between BMI is statistically significant as calculated by the one-way ANOVA (F =256.18, p=5E-57). Since the p-value is less that the critical value then, the null hypothesis should be rejected. Therefore, it is expected that people who have smoking habit will not have a higher BMI index than those who do not (NCD Risk Factor Collaboration, 2016).

Table 3: One -way ANOVA results for current smoking status

The null hypothesis for systolic blood status states that H0 BMI is not related to the systolic blood of a patient. The alternative hypothesis states that H1 BMI is related to the systolic blood of a patient. From the results, it can be concluded that the difference between BMI is statistically significant as calculated by the one-way ANOVA (F =22.26, p=2.41E-06). The p-value is less that the critical value, therefore, the null hypothesis should be rejected. Therefore, the higher the systolic blood, the higher the BMI (NCD Risk Factor Collaboration, 2016).

Table 4: One -way ANOVA results for systolic blood

Table 5: One -way ANOVA results for total serum cholesterol

The null hypothesis for pressure states that H0 BMI is not related to the pressure of a patient. On the other hand, the alternative hypothesis states that H1 BMI is related to pressure states of a patient. From the results, it can be concluded that the difference between BMI is statistically significant as calculated by the one-way ANOVA (F =30.27, p=3.84E-08). The null hypothesis should be rejected Since the p-value is less that the critical value. As BMI index increases, the high blood pressure also increases (Abarca-Gómez et al., 2017).

Table 6: One -way ANOVA results for high blood pressure

Finally, the age of a person also has a great effect on BMI. For the analysis purposes of the data provided, the null hypothesis for age states that H0 BMI is not related to the age of a patient while the alternative hypothesis states that H1 BMI is related to age states of a patient. From the results, it can be concluded that the difference between BMI is statistically significant as calculated by the one-way ANOVA (F =30.27, p=3.84E-08). The null hypothesis should be rejected because the p-value is less that the critical value of 0.05.

Table 7: One -way ANOVA results for Age

Conclusions

From the data analysis above, BMI has a relationship with age, diabetes, current smoking status, total serum cholesterol, high blood pressure, systolic blood and gender. Before beginning the data analysis, all missing information was removed when rearranging it because all data should also have equal variance. A total of 446 missing entries were removed. The one-way ANOVA requires that the data to be normal and samples on which the tests will be applied to be independent. The independent factor was BMI, and the dependent variables were age, diabetes, current smoking status, total serum cholesterol, high blood pressure, systolic blood and gender. There is a similarity between the crude and multivariable effects of BMI. All null hypothesis was rejected when using multivariable analysis. The same could have happened when using crude analysis.

References

Abarca-Gómez, L., Abdeen, Z. A., Hamid, Z. A., Abu-Rmeileh, N. M., Acosta-Cazares, B., Acuin, C., ... & Agyemang, C. (2017). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128· 9 million children, adolescents, and adults. The Lancet, 390(10113), 2627-2642.

Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., ... & Lewington, S. (2016). Body-mass index and all-cause mortality: individual- participant-data meta-analysis of 239 prospective studies in four continents. The Lancet, 388(10046), 776-786.

Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among adults and youth: United States, 2015–2016.

NCD Risk Factor Collaboration. (2016). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19· 2 million participants. The Lancet, 387(10026), 1377-1396.