Patterns of repeated diagnostic testing for COVID‐19 in relation to patient characteristics and outcomes

Abstract

Background Whilst the COVID‐19 diagnostic test has a high false‐negative rate, not everyone initially negative is re‐tested. Michigan Medicine, a primary regional centre, provided an ideal setting for studying testing patterns during the first wave of the pandemic. Objectives To identify the characteristics of patients who underwent repeated testing for COVID‐19 and determine if repeated testing was associated with downstream outcomes amongst positive cases. Methods Characteristics, test results, and health outcomes for patients presenting for a COVID‐19 diagnostic test were collected. We examined whether patient characteristics differed with repeated testing and estimated a false‐negative rate for the test. We then studied repeated testing patterns in patients with severe COVID‐19‐related outcomes. Results Patient age, sex, body mass index, neighbourhood poverty levels, pre‐existing type 2 diabetes, circulatory, kidney, and liver diseases, and cough, fever/chills, and pain symptoms 14 days prior to a first test were associated with repeated testing. Amongst patients with a positive result, age (OR: 1.17; 95% CI: (1.05, 1.34)) and pre‐existing kidney diseases (OR: 2.26; 95% CI: (1.41, 3.68)) remained significant. Hospitalization (OR: 7.88; 95% CI: (5.15, 12.26)) and ICU‐level care (OR: 6.93; 95% CI: (4.44, 10.92)) were associated with repeated testing. The estimated false‐negative rate was 23.8% (95% CI: (19.5%, 28.5%)). Conclusions Whilst most patients were tested once and received a negative result, a meaningful subset underwent multiple rounds of testing. These results shed light on testing patterns and have important implications for understanding the variation of repeated testing results within and between patients.

Understanding the Patterns of Serological Testing for COVID-19 Pre- and Post-Vaccination Rollout in Michigan

“…Similar results are noted with respect to individual sex and comorbidity burden in an early study of IgM-IgG antibody testing for COVID-19 [22]. Differences in diagnostic RT-PCR testing rates and positivity across these individual-level characteristics have also been studied previously, suggesting that similar factors for symptomatic infection may persist as predictors of later seroconversion [6,7,23]. These results shed light on the importance of population-wide serologic testing to inform on the prevalence of the disease, as those tested represent only a selected subset of the general population.…”

“…While individual-level socioeconomic status (SES) was not obtainable, we utilized three metrics of neighborhood SES based on the individual’s residence (2010 census tract) information: the proportion of the census tract population age 16+ in the civilian labor force who were unemployed (neighborhood unemployment), the proportion of the population with an annual income below the federal poverty level (neighborhood poverty), and the proportion of adults with less than a high school diploma (neighborhood education). These data were obtained from the National Neighborhood Data Archive [ 7 ] in addition to the population density of the census tract.…”

“…Recent works have examined COVID-19 outcomes [ 6 ] and testing patterns [ 7 ] for the RT-PCR diagnostic test among a cohort of susceptible individuals presenting to Michigan Medicine, a primary regional health center providing COVID-19 care throughout the pandemic. These previous studies highlighted differences in diagnostic testing, infection, and hospitalization rates with respect to individual demographics, such as age, sex, race, and pre-existing comorbidities.…”

Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes

“…However, much of this early work that reported on the first wave of the pandemic was potentially limited by sample size or follow-up duration [ 26 – 28 ]. Further, most of this work relied on risk factors collected during the early phase of the pandemic [ 29 – 31 ]. As the effects of the pandemic have extended over the past year and into the foreseeable future, and the demographics of new cases are constantly changing [ 32 ], it is pertinent to re-examine these risk factors for adverse outcomes in the context of new information [ 33 ].…”

Adoption of improved neural network blade pattern recognition in prevention and control of corona virus disease-19 pandemic

“…It is generally believed that the richer the number of neurons in the network, the more patterns it can remember and recognize [20] . The characteristic of neural network is that it has good self-adaptive ability and self-organization ability, which changes the weight value of synapse in the process of learning or training to better adapt to the needs of the surrounding environment [21] . The same network has different functions due to differences in learning methods and content.…”