Question

Questions 1, 2, 3, 4, 5, 6, 7, 8 are based on the following information: An epidemiological study (hypothetical) in humans in an infected country showed the following results: 1176 humans from a village were tested for dengue using an ELISA test, 168 tested positive. Out of 168 tested positives, 100 were misclassified as positive. Similarly, 408 were also misclassified as negative by the test. Question 1. Calculate the prevalence based on the ELISA result. 14.3%             b. 10%             c. 13.6%             d. 12.8% Question 2. How many are true positive and true negative? 68 and 600 68 and 1008 700 and 1008 100 and 400 Question 3. What is the sensitivity of the test.             a. 14.3%             b. 15.6%             c. 88%             d. 84% Question 4. What is the specificity of the test?             a. 85.7%             b. 88.2%             c. 92%             d. 99%             Question 5. What is the accuracy of the test?             a. 56.8%             b. 85.7%             c. 64.1%             d. 98.4% Question 6. What is the prevalence based on the true status of the disease?             a. 56.8%             b. 72.0%             c. 69.2%             d. 14.3% Question 7. What is the positive predictive value of the test?             a. 40.5%             b. 59.5%             c. 42.2%             d. 58.4% Question 8. What is the negative predictive value of the test?             a. 59.5%             b. 40.5%             c. 56.8%             d. 69.2%

Answer 1

1. The prevalence based on the ELISA test result is a) 14.3%. It means that according to the present test conducted, only 14.3 % were affected with the disease. It can be calculated by : (Number of cases identified / total number of cases ) *100. In the test, number of cases identified was 168 and total number of cases was 1176.

2. 68 and 600 is the true positives and true negatives. - true positive means the number of patients who tested positive and those who have disease characteristics. In the present study 168 tested positive, but among them only 68 are having disease characteristics. so tru positive is 68 . Similarly, 1008 was tested negative, but in that 408 was misclassified as negative. So in reality only 600 are negative and they are true negatives.

Also, among, 1008 tested negative, 408 is having disease charatceristics and they are called as false negatives. and among 168 tested negatives , 100 tested wrongly and they are called as false positives.

So in the present study , True Positives (TP)- 68

True Negative(TN) - 600

False Positive (FP)- 100

False negative (FN)-408

3. The sensitivity of the test is a) 14.3%. - It can be calculated by: [TP/(TP+FN)]*100. Sensitivity means how likely the test can show positive in someone who has the disease characteristics. In the present study 14.3% is the sensitivity which means that among all people who have disease charatceristics, the test is able to determine only 14.3% cases correctly.

4. The specificity of the test is a)85.7%.- It can be calculated by: [TN/(TN+FP)]*100. Specificity means how likely the test is showing negative in someone who doesnt have the disease characteristics. In the present study the specificity is 85.7%, which means that among all people without disease characteristics, the test could determine 85.7%.

5. The accuracy of the test is a) 56.8%. -It can be calculated by: [(TN+TP) / (TP+TN+FP+FN)]*100. It means that the ability of the test to differentiate the healthy cases and patient cases. in the present study its 56.8% , which reveals that the test is able to differentiate only 68 patient cases and 600 healthy cases correctly among 1176 samples.

6. Prevalence based on true status of disease. The calculated prevalence is 40.5% which is not been included in the choice. It can be calculated by: [(TP+FN) / (TP+FN+TN+FP)] *100.

7. The positive predictive value is a) 40.5%. - The formula to calculate this is : [TP / (TP+FP)] *100. It means that the probability to have the disease characteristics if the test is positive. In the present study , the positive predictive value is 40.5% which means that among all people whose test comes out positive , 40.5 % truly has thecharacteristics .

8. The negative predictive value is a) 59.5%. - It can be calculate by: [TN / (TN+FN) ] *100. It means the probability to not to have the disease characteristics if the test is negative. In the present study , the negative predictive value is 59.5%, which means that among all people whose test revealed negative , 59.5% truly doesnt have disease characteristics.