![]() How rare (or common) a disease is will also affect how accurate your lab test might be. How does disease prevalence affect lab-result accuracy? A manufacturer usually can’t increase the sensitivity of a test without decreasing its specificity - and vice versa. Typically, a test is either better at catching positives or at catching negatives. That means up to 30% to 50% of positive results could be for people who don’t actually have the flu. Flu tests, for example, have a sensitivity between 50% and 70%. If the sensitivity for a test is 90%, that means out of 100 people who are truly positive, it will correctly identify 90 of them. If a test is not good at sensing if you have the condition of interest, it’s more likely to tell you that you do not have the condition when you actually have the condition. Here’s an example of a false-negative result: You test negative on a rapid COVID antibody test, but you actually have COVID. False negativesĪ “false negative” is a negative result when it’s actually a positive result. And 10 of them will get a false-positive result. If the specificity for a test is 90%, that means out of 100 people who are truly negative, it will correctly identify 90 of them as negative. If it’s not good at sensing that you don’t have the condition of interest, it’s more likely to tell you that you have the condition when you don’t. How often a test makes this mistake has to do with how well the test can correctly identify negative cases. For example, if a pregnancy test reports that you’re pregnant but you aren’t actually pregnant, this is a false positive. ![]() False positivesĪ “false positive” is when a lab result says you have something - or something is present - when you don’t actually have it. But it’s an important part of understanding the accuracy of your lab tests. But what about “false positives” and “false negatives" in the context of lab results? This stuff can be a bit more complex than meets the eye. And a “negative” result means that you don’t have what the test is looking for. You may already know that a “positive” result means that you have what the test is looking for. To calculate this accuracy rate, scientists have to consider how often the test gets the positive results correct as well as how often the test gets the negative results correct. A test with 90% accuracy is expected to be correct in its results 90% of the time. “Accuracy” means how well the test can correctly identify positive and negative cases. It’s also important to know that calculating accuracy is not straightforward. We get more into the factors that affect accuracy below. And every test has a different accuracy rate. It’s nearly impossible for a test to be right 100% of the time. We’re here to break down what goes into the concept of testing accuracy and how you can maximize your chances of getting valid results. So, before you hang your hat on your lab results, it helps to know the chances that the results may not be accurate. Some tests are accurate nearly 100% of the time, while others are well known for being unreliable. But certain factors can affect the accuracy of the test - like the test manufacturer and what the test is for. When you get a clinical lab test, it’s natural to take your results as hard facts.
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