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# Describe Epidemiological Data Sources And Measurements (Case Study Sample)

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Case Assignment
1. A doctoral student is conducting a non-concurrent cohort study on the risk factors of a chronic disease. Tables 1 and 2 provide a summary of data that was collected on the 19,753 patients. Table 1 present the results of three Factors (A17, B24, C32)—note that these are just variable names—and they could represent variables such as age, race, sex, etc. Table two presents results by smoking level. Table 3 provides a summary of the multivariate analysis of the three variables and—in this case since the outcome is dichotomous (i.e. either you have the disease or you don't) we can use logistic regression model to study mulitple factors (such as A17 and different smoking levels) on the outcome and come up with adjusted Odds Ratios.
Table 1

Risk Factor Present
Risk Factor Absent
Factor
Total
Patients with Chronic Disease
Total
Patients with
Chronic Disease
Factor A17
2785
26
16968
170
Factor B24
2009
32
17744
164
Factor C32
641
13
19112
183
Table 2

Total Patients
Number of Patients with Chronic Disease
Smoking
None
1–10 cigarettes/day
11–20 cigarettes/day
21–30 cigarettes/day
>30 cigarettes/day
5030
6061
4572
2999
1091
15
21
43
56
61

Table 3
Multivariate Analysis
Factor
95% CI
Factor A17
0.86
0.55–1.29
Factor B24
1.50
0.97–2.27
Factor C32
1.98
1.03–3.50
A. Which measure of association would you use for this study? Why?
B. Using bivariate analysis, calculate the measure of association between the chronic disease and the following theoretical risk factors: A17, B24, C32, and smoking (at the different exposure levels). Provide 95% confidence intervals.
C. In narrative form, provide a description of the results for the bivariate and multivariate analysis.
D. Provide discussion and interpretation of the results from the bivariate and multivariate analysis. For the multivariate analysis explain why Odds Ratios are being used with Cohort data.
2. A second doctoral student is conducting a case control study on risk factors for an infectious disease and has collected the data below. Using bivariate analysis, calculate the measure of association between the disease and risk factor studied for each level of exposure. Provide 95% confidence intervals. Provide an interpretation of the results.
Factor BF
Case (n=186)
Control (n=277)
Brand Y
Brand X—50 mg
Brand X—100 mg
Brand X—200 mg
15
11
28
46
86
78
35
70
50
44
*If you do not have access to a statistical software package such as SPSS, the following are online calculators that can be used for this assignment: 1) Odds Ratio Calculator, 2) Relative Risk Calculator or An optional calculator is available at http://statpages.org/ctab2x2.html

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A. Which measure of association would you use for this study? Why?
The measure of association that can be used in this study is the Rate Ratio. This is relative to the fact that is allows for comparison between the different groups when making the analysis. This uses the primary group in comparison to the comparison group. Giving a ration that can then be used to interpret the level of risk between the two groups ("Correlation Coefficient: Simple Definition, Formula, Easy Steps", 2017).
B. Using bivariate analysis, calculate the measure of association between the chronic disease and the following theoretical risk factors: A17, B24, C32, and smoking (at the different exposure levels). Provide 95% confidence intervals.
A bivariate analysis of the level of exposure to smoking compared to the factors A17, B24 and C32 indicates that, there is a positive correlation ("Correlation Coefficient: Simple Definition, Formula, Easy Steps", 2017). The correlation coefficient at the confidence level of 95% is o.4.
C. In narrative form, provide a description of the results for the bivariate and multivariate analysis.
According to the results, the positive coefficient on the correlation between the level of exposure and the persons that were considered to be suffering from the chronic diseases, this is a clear indication that smoking leads to disease. What is important is the fact that the 0.4 of the correlation coefficient is a positive figure (Centers for Disease Control and Prevention, 2017). This is to mean that the c

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