How do the UK public interpret COVID-19 test results? Comparing the impact of official information about results and reliability used in the UK, USA and New Zealand: a randomised controlled trial

Objectives To assess the effects of different official information on public interpretation of a personal COVID-19 PCR test result. Design A 5×2 factorial, randomised, between-subjects experiment, comparing four wordings of information about the test result and a control arm of no additional information; for both positive and negative test results. Setting Online experiment using recruitment platform Respondi. Participants UK participants (n=1744, after a pilot of n=1657) quota-sampled to be proportional to the UK national population on age and sex. Interventions Participants were given a hypothetical COVID-19 PCR test result for ‘John’ who was presented as having a 50% chance of having COVID-19 based on symptoms alone. Participants were randomised to receive either a positive or negative result for ‘John’, then randomised again to receive either no more information, or text information on the interpretation of COVID-19 test results copied in September 2020 from the public websites of the UK’s National Health Service, the USA’s Centers for Disease Control, New Zealand’s Ministry of Health or a modified version of the UK’s wording. Information identifying the source of the wording was removed. Main outcome measures Participants were asked ‘What is your best guess as to the percent chance that John actually had COVID-19 at the time of his test, given his result? ’; questions about their feelings of trustworthiness in the result, their perceptions of the quality of the underlying evidence and what action they felt ‘John’ should take in the light of his result. Results Of those presented with a positive COVID-19 test result for ‘John’, the mean estimate of the probability that he had the virus was 73% (71.5%–74.5%); for those presented with a negative result, 38% (36.7%–40.0%). There was no main effect of information (wording) on these means. However, those participants given the official information from the UK website, which did not mention the possibility of false negatives or false positives, were more likely to give a categorical (100% or 0%) answer (UK: 68/343, 19.8% (15.9%–24.4%); control group: 42/356, 11.8% (8.8%–15.6%)); the reverse was true for those viewing the New Zealand (NZ) wording, which highlighted the uncertainties most explicitly (20/345: 5.8% (3.7%–8.8%)). Aggregated across test result (positive/negative), there was a main effect of wording (p<0.001) on beliefs about how ‘John’ should behave, with those seeing the NZ wording marginally more likely to agree that ‘John’ should continue to self-isolate than those viewing the control or the UK wording. The proportion of participants who felt that a symptomatic individual who tests negative definitely should not self-isolate was highest among those viewing the UK wording (31/178, 17.4% (12.5%–23.7%)), and lowest among those viewing the NZ wording (6/159, 3.8% (1.6%–8.2%)). Although the NZ wording was rated harder to understand, participants reacted to the uncertainties given in the text in the expected direction: there was a small main effect of wording on trust in the result (p=0.048), with people perceiving the test result as marginally less trustworthy after having read the NZ wording compared with the UK wording. Positive results were generally viewed as more trustworthy and as having higher quality of evidence than negative results (both p<0.001). Conclusions The public’s default assessment of the face value of both the positive and negative test results (control group) indicate an awareness that test results are not perfectly accurate. Compared with other messaging tested, participants shown the UK’s 2020 wording about the interpretation of the test results appeared to interpret the results as more definitive than is warranted. Wording that acknowledges uncertainty can help people to have a more nuanced and realistic understanding of what a COVID-19 test result means, which supports decision making and behavioural response. Preregistration and data repository Preregistration of pilot at osf.io/8n62f, preregistration of main experiment at osf.io/7rcj4, data and code available online (osf.io/pvhba).