We need signs which detect the highest number of cases with the illness (high sensitivity) and, at the same time, do not include those without that illness.
Sensitivity: Proportion of cases with the illness which are correctly detected by that sign/s (true positives).
Example: Let’s take 100 children with pneumonia. If fast breathing is present and correctly identifies 80 out of these 100 children with pneumonia, it has a sensitivity of 80%. These 80 children with fast breathing correctly identified are “true positives”. In this example, fast breathing is not present in the remaining 20 children with pneumonia, who would therefore be missed if we relied only on fast breathing. These 20 children missed are “false negatives”. So:
Children with pneumonia (denominator): 100
Children with fast breathing (numerator): 80 (true positives: correctly identified)
Sensitivity of fast breathing: 80/100 = 80%
[Children without fast breathing: 20 (false negatives: missed)]
Thus, if a “true positive” is a child with pneumonia correctly identified by fast breathing, and a “false negative” is a child with pneumonia missed because of the absence of fast breathing:
Sensitivity = true positives / (true positives + false negatives)
Note: sensitivity does not depend on the prevalence of the illness or condition in the population.
To sum up, sensitivity tells us what percentage of children with the illness we can identify (and, on the other hand, what percentage we would miss). Clinicians’ concern is usually to miss as few children with the condition as possible (requirement for high sensitivity).
We need signs which detect the highest number of cases with the illness (high sensitivity) and, at the same time, do not include those without that illness.
Sensitivity: Proportion of cases with the illness which are correctly detected by that sign/s (true positives).
Example: Let’s take 100 children with pneumonia. If fast breathing is present and correctly identifies 80 out of these 100 children with pneumonia, it has a sensitivity of 80%. These 80 children with fast breathing correctly identified are “true positives”. In this example, fast breathing is not present in the remaining 20 children with pneumonia, who would therefore be missed if we relied only on fast breathing. These 20 children missed are “false negatives”. So:
Children with pneumonia (denominator): 100
Children with fast breathing (numerator): 80 (true positives: correctly identified)
Sensitivity of fast breathing: 80/100 = 80%
[Children without fast breathing: 20 (false negatives: missed)]
Thus, if a “true positive” is a child with pneumonia correctly identified by fast breathing, and a “false negative” is a child with pneumonia missed because of the absence of fast breathing:
Sensitivity = true positives / (true positives + false negatives)
Note: sensitivity does not depend on the prevalence of the illness or condition in the population.
To sum up, sensitivity tells us what percentage of children with the illness we can identify (and, on the other hand, what percentage we would miss). Clinicians’ concern is usually to miss as few children with the condition as possible (requirement for high sensitivity).
