6. 'तीसरी कसम' की नायिका हीरा बाई का चरित्र-चित्रण कीजिए।
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now testing people without symptoms as part of efforts to manage COVID-19. In Victoria, asymptomatic health-care workers have been part of the recent “testing blitz”.
We tend to take for granted that the results of medical tests are accurate – but no test is perfect and all carry a risk of harm of some kind. Although there has been a drive to increase testing, we must recognise this is also true for coronavirus.
All tests have limitations
Among the shortfalls of diagnostic testing is the possibility of false negatives (failing to detect a condition when it’s present) and false positives (detecting a condition when it’s absent).
It’s easy to see why false negatives can be a problem – we lose the benefits of early intervention.
But false positives can also cause harm, including unnecessary treatment. This is why positive screening tests are often followed up with a second, different test to confirm a diagnosis.
Examples include further imaging and possibly biopsy following a positive mammogram for breast cancer, or colonoscopy following positive screening for colon cancer.
Why do we get false positives?
False positives can occur for many reasons, including normal human and system errors (for example mislabelling, data entry errors or sample mishandling).
Sometimes false positive test results could be due to a cross-reaction with something else in the sample, such as a different virus.
Data entry errors can lead to false positives or false negatives. Shutterstock
For COVID-19, the only routinely available option to confirm a positive result is to retest using the same method. This can address the false positives generated through sample contamination or human error.
Even so, some authorities recommend isolation for any person who returns a positive test, regardless of subsequent results.
Testing more widely could mean more false positives
The proportion of false positives among all positive results depends not just on the characteristics of the test, but on how common the condition being tested for is among those being tested.
This is because even a highly specific test – one that generates hardly any false positives – may still generate more false positive results than there are actual cases of the condition in those being tested (true positives).
Let’s work through an example.
Say we have a very good test which is 99.9% specific – that is, only one in 1,000 tests give a false positive. And imagine we’re testing 20,000 people for condition X. Condition X has a very low prevalence – we estimate it affects 0.01%, or one in 10,000 people in the population.
At this level we could expect two joaidbdjsnwjs sjsjwbw