U.K. and U.S. residents who reported a loss of smell and taste into a COVID-19 symptom-tracker app more often tested positive for the infection, according to a study published yesterday in Nature.

The COVID Symptom Study app was developed by health technology company Zoe Global with help from King's College London and Massachusetts General Hospital. It launched in the U.K. and the U.S. in late March, and after three weeks had gained more than 2.6 million users.

By applying a predictive model incorporating these testing rates to the hundreds of thousands of users who reported symptoms without testing data, the researchers estimated that a little over 5% of those who downloaded the app "are likely to have COVID-19." However, the researchers cautioned that this is likely an overestimation due to a handful of factors, such as the use of self-reported symptom data.

TOPLINE RESULTS

A total of 2,6618,862 residents from the U.K. (n = 2,450,569) and U.S. (n =168,293) downloaded the free study app and reported potential COVID-19 symptoms. Of these, 15,638 from the U.K. and 2,763 from the U.S. paired their reported symptoms with the results of a RT-PCR SARS-CoV-2 test.

Among the U.K. cohort, nearly two thirds of those who reported positive test results also reported a loss of smell and taste, compared to the roughly 23% of negative cases who reported the same symptoms, yielding an odds ratio [OR] of 6.4 (p < .0001). Combining these rates with the U.S. cohort's OR of 10.01 (p < .0001) produced an overall OR of 6.74 (P < .0001) after adjustments.

"We report that loss of smell and taste is a potential predictor of COVID-19 in addition to other, more established symptoms including high temperature and a new, persistent cough," the researchers wrote. "COVID-19 appears to cause problems of smell receptors in line with many other respiratory viruses, including previous coronaviruses thought to account for 10%–15% of cases of anosmia."

The analysis also found 10 other symptoms such as fever, fatigue, shortness of breath and diarrhea to be associated with positive diagnostic test among the U.K. cohort, while only loss of smell and taste, fatigue and skipped meals were associated among the U.S. cohort.

From these findings, the researchers build a symptoms-prediction model to estimate the number of untested participants (n = 805,753) who likely had the disease. According to the model, 140,312 (17.42%) symptom-reporting participants likely were infected, representing 5.36% of the full study cohort.

HOW IT WAS DONE

The COVID Symptom Study app was released for free in the U.K. on March 24, and in the U.S. on March 29 to residents with or without apparent COVID-19 symptoms. Users record their location, age and core health-risk factors upon initial use, and then update the tool daily with their symptoms, healthcare encounters, diagnostic test results and quarantine behaviors.

Relationships between symptoms and COVID-19 test results were identified via multivariate logistic regression with adjustments for age, sex and BMI.

The researchers highlighted a handful of limitations inherent to their study design, chief among which were the reliance on self-reported data, rather than physiologic assessment, and participant self-enrollment. Further, because SARS-CoV-2 testing is more common among those with likely cases, the model's training sample was not representative of the general population.

THE LARGER TREND

The last couple of months have seen a wave of digital symptom-checkers, like the COVID Symptom Study app, released by technology companies, health systems and governments alike. In fact, just this weekend the World Health Organization shared plans for a symptom-checker app that it will be providing to countries without the resources to build and distribute their own digital tool.

The debate over whether these apps and others for contact tracing are appropriate (or even effective) is ongoing. Albeat its sampling caveats, the Nature study offers an example of how large-scale data collection could be used to help public health identify predictors of the disease and understand its potential spread.