What you need to know to understand your Covid test result

Testing was crucial during the pandemic. Negative COVID-19 test results have given people the freedom to work and travel, while positive results have been used to isolate infected people and protect the wider community.

However, the main form of tests used – the laboratory-based method known as PCR testing – is not accurate. False positives, where a test says someone has COVID-19 when they don’t intended to perform between 0.8% and 4% of the cases where people without COVID-19 are tested.

Estimates of the opposite – false negatives – vary widely (making them uncertain). According to reports from 1.8% to 58% of tests done on people who actually have COVID-19, they falsely give a negative result a far-reaching review.

However, whether or not you will get this type of information when doing a PCR test depends on the guidelines of your health care provider or government. This is important because, as I and my colleagues have found, the way this uncertainty is communicated about the tests affects the understanding of the test results.

This, in turn, has the potential to influence decisions about whether to isolate or not. In a pandemic, this can have serious health consequences.

In our In a study we presented 1,744 residents of Great Britain – Sample that is proportional to the national population in terms of age and gender – with a hypothetical scenario. Attendees were told that a man named John felt sick and that a knowledgeable doctor believes John has a 50:50 chance of COVID-19 based on his symptoms alone. So John dutifully does a PCR test.

Roughly half of the participants were told that John had tested positive and the other half tested negative. We then showed each participant explanatory information from the New Zealand Department of Health, the United States Centers for Disease Control (CDC) or the UK National Health Service (NHS), or a modified version of the NHS message, or no message at all.

The original NHS guidelines only stated that “a negative result means the test did not find coronavirus,” while the CDC and modified NHS guidelines have somewhat secured themselves. For example, the CDC guidelines state, “If you test negative, you probably weren’t infected when the sample was collected,” and it was recommended that test recipients continue to take steps to protect themselves.

The New Zealand Department of Health was more explicit about false positives and false negatives and the difference between them. It is Instructions said While false negatives are rare in laboratory studies, “it’s important to remember that tests don’t work as well in the real world”. It then provided reasons why false negative results can occur. In contrast, it found that “very few (if any) false positive test results were expected”.

We found that people who read guidelines without confirmation or discussion of false positives or negatives are the most likely to believe that the test result cannot be false. That said, they most likely believed that a positive result meant that the hypothetical patient John had a 100% chance of getting COVID-19, or that a negative result meant a 0% chance that he had it – which he didn’t was the case.

It was also more likely they would “disagree” that John should self-isolate if he tested negative – when in reality there was a chance he was still having the virus. Given that we had described John as “sick,” ideally people would have said he should stay home regardless of his test result.


Research suggests that how and if uncertainty is communicated to us can change our beliefs and behavior in situations that can be a matter of life or death.

People who write guidelines sometimes fear that highlighting greater uncertainties can lead to a loss of confidence, and it can do so in some contexts. For example, research found that reading about a scientific model that had extremely high uncertainty about the number of future COVID-19 deaths resulted in less confidence in science than reading about a more specific model. On the other hand, reading a particular prediction and then learning that they differed significantly also decreased confidence.

Recognizing and contextualizing uncertainties without overemphasis can represent a middle ground. It can demonstrate trustworthiness and in this way help to maintain at least some trust even when predictions or test results are wrong.

Fortunately, since our research was done, the NHS website has moved away from the assumption that there is no certainty in PCR testing and updated its guidelines to say that a negative test “does not guarantee you will not have COVID-19”.

Gabriel Recchia, Research Associate, Winton Center for Risk and Evidence Communication, Cambridge University

This article is republished by The conversation under a Creative Commons license. read this original article.


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