This paper analyzes data of three hundred individuals’ health records. The data summarizes the diabetes status of the sample and presents their medical, social, physical and economic characteristics. Previous public-health studies have investigated and identified the relationship between variables and the prevalence of diabetes. The results will be compared with the findings of previous research on diabetes.
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Ten variables used to analyze the results were gender, race, salary, education, height, weight, Body Mass Index (BMI), allergies, family history of diabetes, and family history of allergies. Subsequent sections analyze the relationships between the variables.
The relationship between each variable and diabetes may be observed by comparing the mean values for the two groups. Table 1 summarizes the mean values of the numerical variables for participants in the two groups.
Table 1: Descriptive Statistics of Numerical Variables
Two findings are deducible from the results presented in the table. The average age of those with diabetes was 70.47. However, those without diabetes had an average age of 38.95 years. This finding is supported by a previous study which reported that the decline in protein synthesis in aging tissues increased the risks of diabetes in older people (Yamamoto, et al., 2014).
The effect of income on diabetes is observed in the table. The average salary of those with diabetes is $45,522 and for those without diabetes is $70,226.45. These findings can be compared with previous research that suggested low-income earners were more likely to have diabetes than their wealthier counterparts (Lysy, et al., 2013).
Although discrepancies can be observed in the mean scores between the two groups, it is important to test the level of significance of these variations. Chi-Square tests and t-tests were used to investigate the significance of the difference in mean values. Table 2 summarizes the results of the Chi-square test. The p-values for salary (0.001), height (0.000), weight (0.000), BMI (0.000), family history of diabetes (0.000), and family history of allergies (0.000) showed the variables were related to diabetes.
The results are similar to previous studies, which suggest that income (Lipscombe, Austin & Manuel, 2010), physical characteristics (Narayan & Boyle, 2007), and family history (Uusitupa, Stancakova, Peltonen, & Eriksson, 2011) are related to diabetes.
Table 2: Chi-square values and p-values
|Family history diabetes||143.728||.000|
|Family history allergies||166.699||.000|
Table 3 summarizes the p-values derived from the t-test. The findings are similar to the p-values derived from the chi-square tests however the p-value for age (0.000) derived from the t-test suggests a significant relationship between age and diabetes (Creatore, Moineddin & Booth, 2010).
Table 3: t-test values and p-values
|Family history diabetes||-16.555||.000|
|Family history allergies||19.304||.000|
Creatore, M. I., Moineddin, R., & Booth, G. (2010). Age- and sex-related prevalence of diabetes mellitus among immigrants to Ontario, Canada. CMAJ, 182(8), 781-789.
Lipscombe, L. L., Austin, P. C., & Manuel, D. G. (2010). Income-related differences in mortality among people with diabetes mellitus. CMAJ, 182(1), E1-E17.
Lysy, Z., Booth, G., Shah, B., Austin, P., Luo, J., Lipscombe, L. (2013). The impact of income on the incidence of diabetes: a population-based study. Diabetes Research for Clinical Practice, 99(3), 372-379.
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Narayan, K. M., & Boyle, J. P. (2007). Effect of BMI on lifetime risk for diabetes in the U.S. Diabetes Care, 30(6), 1562-1562.
Uusitupa, M. I., Stancakova, A., Peltonen, M., Eriksson, J. G. (2011). Impact of Positive Family History and Genetic Risk Variants on the Incidence of Diabetes. Diabetes Care, 34(2), 418–423.
Yamamoto, K., Kitano, Y., Shuang, E., Hatakeyama, Y., Sakamoto, Y., Honma, T., & Tsuduki, T. (2014). Decreased lipid absorption due to reduced pancreatic lipase activity in aging male mice. Biogerontology, 15(5), 463-473.