Wk 1 We are exploring sensitivity and specificity this week

Posted: May 5th, 2020

Wk 1 We are exploring sensitivity and specificity this week. This can let you know the limitations of a test for a disease. More and more labs, hospitals, and public health agencies are moving to molecular methods to test for disease, but often for screening tests we use test like EIA or other rapid immunogenic tests.
Your goal:
Select a disease
• Find the prevalence of that disease in the US
• Find a screening test for that disease and the manufacturer’s reported sensitivity and specificity
• Apply that proportion to a random population of 100,000 people
• Calculate the predictive positive value and the predictive negative value
• Answer the question: Does it make sense to screen for this disease using this test and based on the natural history of the disease why or why not?
Example: How to work the math alone
Disease X occurs at a prevalence of 10 per 100,000 population.
Screening test Y has a sensitivity of 95% and a specificity of 80%.
Step 1:
Disease (+) Disease (-)
Test (+) 95% 20%
Test (-) 5% 80%
10 999,990
Step 2:
Disease (+) Disease (-)
Test (+) 9.5 199,998
Test (-) .5 799,992
10 999,990

From this point, you can easily calculate the predictive positive and the predictive negative values. The real challenge is going to look at the natural history of the disease, cost of the test, treatment of the disease and determine if screening is appropriate. If it is appropriate or not, you have to provide researched justification as to why it is not or is and if there are certain groups it would be best to screen or prioritize why or why not.
All research should be cited and appropriately supported.
The initial post must be between 350 – 500 words.

_____________________-
For the purpose of this exercise, let’s select breast cancer as the disease to analyze. Breast cancer is a common form of cancer that affects both men and women. It is important to note that the data used in this analysis is based on the knowledge cutoff of September 2021, and more recent information may be available.

Step 1: Prevalence of Breast Cancer in the US
According to the American Cancer Society, in 2021, an estimated 281,550 new cases of invasive breast cancer were expected to be diagnosed among women in the United States. Additionally, about 2,650 new cases were estimated for men. These figures provide us with a prevalence rate of approximately 284,200 cases per year.

Step 2: Screening Test and Reported Sensitivity/Specificity
Mammography is one of the most common screening tests used for breast cancer. It is a low-dose X-ray examination of the breasts and has been proven effective in detecting early signs of breast cancer. The reported sensitivity and specificity of mammography vary depending on various factors such as age and breast density.

According to a meta-analysis published in the New England Journal of Medicine, the estimated sensitivity of mammography for detecting breast cancer ranged from 60% to 98%, with an average of approximately 85%. The specificity ranged from 94% to 97%, with an average of 96%.

Step 3: Application to a Random Population
Let’s apply the proportions of sensitivity and specificity to a random population of 100,000 people to calculate the predictive values.

Disease (+) Disease (-)
Test (+) 85,000 (85% of 100,000) 4,000 (4% of 100,000)
Test (-) 15,000 (15% of 100,000) 96,000 (96% of 100,000)

Step 4: Calculation of Predictive Values
Predictive Positive Value (PPV) is the proportion of individuals who test positive and actually have the disease. It is calculated as the number of true positives divided by the sum of true positives and false positives.

PPV = True Positives / (True Positives + False Positives)
PPV = 85,000 / (85,000 + 4,000)
PPV = 0.955 (or 95.5%)

Predictive Negative Value (NPV) is the proportion of individuals who test negative and do not have the disease. It is calculated as the number of true negatives divided by the sum of true negatives and false negatives.

NPV = True Negatives / (True Negatives + False Negatives)
NPV = 96,000 / (96,000 + 15,000)
NPV = 0.865 (or 86.5%)

Step 5: Evaluation of Screening for Breast Cancer
When considering whether it makes sense to screen for breast cancer using mammography, several factors need to be taken into account.

Natural History of the Disease: Breast cancer can progress and become more difficult to treat as it advances. Detecting the disease early through screening allows for earlier intervention, which can significantly improve outcomes and increase the chances of successful treatment.

Cost of the Test: Mammography is generally covered by health insurance plans in the United States, but out-of-pocket costs may still be a barrier for some individuals. Access to affordable and readily available screening tests is crucial for effective screening programs.

Treatment Options: Breast cancer has various treatment options available, including surgery, radiation therapy, chemotherapy, and targeted therapies. Early detection through screening enables patients to receive timely and appropriate treatment, potentially improving survival rates.

Age and Risk Factors: Screening guidelines for breast cancer take into