The use of statistics in epidemiology helps to determine differences in risk rel

Question

The use of statistics in epidemiology helps to determine differences in risk related to exposures and permits evaluation of associations with respect to validity to strengthen study conclusions. Imagine you are an epidemiologist investigating the development of lung cancer associated with occupational exposures to pesticides. Employment records identify 57,310 individuals who have worked in a facility where the pesticide is present. Of the 57,310 individuals, you are able to locate 28,732 individuals, 654 of whom have lung cancer.

Your investigative team decides to conduct an occupational surveillance program at a facility to identify people who have excessive pesticide exposures. You screen 5,456 workers using a new diagnostic test, and 1,158 workers with excessive pesticide exposure test positive. You observed a negative test finding in 64 workers who had excessive exposure to pesticides, and 218 workers without excessive pesticide exposure tested positive.

1.Identify potential sources of bias in this study.

2.Describe how researchers could address these potential sources of bias in future studies.

3.Based on the results of the screening test, construct a 2 x 2 table and calculate the following measures:

Sensitivity

Specificity

Prevalence of excessive benzene exposure

Positive predictive value

Negative predictive value

Accuracy

4.Based on the results of the screening program, what conclusions would you draw regarding the new diagnostic test?

Please make sure you answer each part of the question using complete sentences and proper spelling and grammar. Make sure your answers are thorough, yet concise, and that you support your responses with at least two appropriately cited references. Wikipedia is not an acceptable reference

Explanation / Answer

1. A good test will have minimal numbers in cells False positive and False negative.

False positive identify individuals without disease but for whom the test indicates 'disease'. These are false positives.

Another cell has the false negatives. These are ones who are identified as exposed ones but results are negative.

2. They should find ways to minimize the values in these two cells. Any potential system designed should be verified with all the 4 parameters described in the 2*2 table below.

3.

If

Sensitivity: A/(A+C) × 100 = 94.7

Specificity: D/(D+B) × 100 = 94.8

Prevalence of excessive benzene exposure = T exposed/ Total × 100 = 0.2239*100 = 22.39

Positive Predictive Value: A/(A+B) × 100 = 84.1

Negative Predictive Value: D/(D+C) × 100 = 98.4

4. Specificity and sensitivity are at 95%

Validity is measured by sensitivity and specificity.

prevalence and predictive value are sited in various sites

References:

https://onlinecourses.science.psu.edu/stat507/node/71

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2636062/

Test result/Truth exposed non exposed Total Positive True positive False positive T Test positive Negative False negative True negative T test negative T exposed T non exposed

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