Background: COVID-19 positivity rates reported to the public may provide a distorted view of community trends because they tend to be inflated by high-risk groups, such as symptomatic patients and individuals with known exposures to COVID-19. This positive bias within high-risk groups has also varied over time, depending on testing capability and indications for being tested. In contrast, throughout the pandemic, routine COVID-19 screening tests for elective procedures and operations unrelated to COVID-19 risk have been administered by medical facilities to reduce transmission to medical staffing and other patients. We propose the use of these pre-procedural COVID-19 patient datasets to reduce biases in community trends and better understand local prevalence. Methods: Using patient data from the Maui Medical Group clinic, we analyzed 12,640 COVID-19 test results from May 1, 2020 to March 15, 2021, divided into two time periods corresponding with Maui's outbreak. Results: Mean positivity rates were 0.1% for the pre-procedural group, 3.9% for the symptomatic group, 4.2% for the exposed group, and 2.0% for the total study population. Post-outbreak, the mean positivity rate of the pre-procedural group was significantly lower than the aggregate group (all other clinic groups combined). The positivity rates of both pre-procedural and aggregate groups increased over the study period, although the pre-procedural group showed a smaller rise in rate. Conclusions: Pre-procedural groups may produce different trends compared to high-risk groups and are sufficiently robust to detect small changes in positivity rates. Considered in conjunction with high-risk groups, pre-procedural marker groups used to monitor understudied, low-risk subsets of a community may improve our understanding of community COVID-19 prevalence and trends.
[1]
S. Georganas,et al.
Debiasing Covid-19 prevalence estimates
,
2021
.
[2]
M. Samore,et al.
What Is the Active Prevalence of COVID-19?
,
2020,
SSRN Electronic Journal.
[3]
M. Poljak,et al.
Low prevalence of active COVID-19 in Slovenia: a nationwide population study of a probability-based sample
,
2020,
Clinical Microbiology and Infection.
[4]
O. Brynildsrud.
COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence
,
2020,
BMC Medical Research Methodology.
[5]
G. Chowell,et al.
Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate
,
2020,
International Journal of Infectious Diseases.
[6]
M. Busch,et al.
Prevalence of HIV-1 in blood donations following implementation of a structured blood safety policy in South Africa.
,
2006,
JAMA.