Real world outcomes of distributing Lucira Check-It® COVID self-tests in Ontario, Canada: the GetaKit COVID study

In Ontario, Canada we developed and implemented an online screening algorithm for the distribution of HIV self-tests, known as GetaKit. During the COVID pandemic, we adapted the GetaKit algorithm to screen for COVID based on population and infection data and distributed COVID rt-LAMP self-tests (using the Lucira Check-It®) to eligible participants.

GetaKit/COVID was a prospective observational study that occurred over a 7-month period from September 2021 to April 2022. All potential participants completed an online registration and risk assessment, including demographic information, COVID symptoms and risk factors, and vaccination status. Bivariate comparisons were performed for three outcomes: results reporting status, vaccination status, and COVID diagnosis status. Data were analysed using Chi-Square for categorial covariates and Independent Samples T-Test and Mann-Whitney U test for continuous covariates. Bivariate logistic regression models were applied to examine associations between the covariates and outcomes.

During the study period, we distributed 6469 COVID self-tests to 4160 eligible participants; 46% identified as Black, Indigenous or a Person of Colour (BIPOC). Nearly 70% of participants reported their COVID self-test results; 304 of which were positive. Overall, 91% also reported being vaccinated against COVID. Statistical analysis found living with five or fewer people, having tested for COVID previously, and being fully vaccinated were positive factors in results reporting. For COVID vaccination, people from large urban centers, who identified their ethnicity as white, and who reported previous COVID testing were more likely to be fully vaccinated. Finally, being identified as a contact of someone who had tested positive for COVID and the presence of COVID-related symptoms were found to be positive factors in diagnosis.

While most participants who accessed this service were vaccinated against COVID and the majority of diagnoses were identified in participants who had symptoms of, or an exposure to, COVID, our program was able to appropriately link participants to recommended follow-up based on reported risks and results. These findings highlight the utility of online screening algorithms to provide health services, particularly for persons with historical barriers to healthcare access, such as BIPOC or lower-income groups.