Potential Biases Arising From Epidemic Dynamics in Observational Seroprotection Studies

Clinical characteristics and outcomes of COVID-19 cases admitted to adult intensive care units during the pandemic: A single center experience

BACKGROUND

COVID-19 is the largest recorded pandemic in history. It causes several complications such as shock, pneumonia, acute respiratory distress syndrome, and organ failure. The objective was to determine COVID-19 outcomes and risk factors in the intensive care (ICU) setting.

METHODS

A retrospective review of prospectively collected data was conducted. Adult patients with a positive RT-PCR test for COVID-19 admitted to ICUs of a tertiary care hospital between 2020 and 2022 were included. Patients who had severe complex trauma were excluded. The outcomes examined included ventilation use and duration, length of stay (LOS), and mortality.

RESULTS

A total of 964 patients were included. The mean ( ± standard deviation, SD) age was 63.7 ± 16.9 years. The majority of the patients were males (59.0 %) and Saudi (75.7 %). Ventilation use was documented in 443 (57.1 %) patients, with a mean ( ± SD) ventilation duration of 9.7 ± 8.4 days. Death occurred in 361 (37.4 %) patients after a mean ( ± SD) of 33.3 ± 44.5 days from infection. The mean ( ± SD) LOS was 30.6 ± 54.1 days in hospital and 5.2 ± 5.4 days in ICU. Ventilation use was associated with older age, males, longer ICU LOS, mortality, and admission to medical-surgical ICU. Crude mortality use was associated with older age, longer ICU LOS, use of ventilator, shorter ventilation duration, and admission to medical-surgical or respiratory ICUs.

CONCLUSIONS

COVID-19 patients admitted to adult ICUs are at high risk of death and mechanical ventilation. The crude risks of both outcomes are higher in older age and longer ICU LOS and are very variable by ICU type.

ADDRESSING SELECTION BIAS AND MEASUREMENT ERROR IN COVID-19 CASE COUNT DATA USING AUXILIARY INFORMATION

Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal information on the COVID-19 pandemic. While expanded testing is laudable, measurement error and selection bias are the two greatest problems limiting our understanding of the COVID-19 pandemic; neither can be fully addressed by increased testing capacity. In this paper, we demonstrate their impact on estimation of point prevalence and the effective reproduction number. We show that estimates based on the millions of molecular tests in the US has the same mean square error as a small simple random sample. To address this, a procedure is presented that combines case-count data and random samples over time to estimate selection propensities based on key covariate information. We then combine these selection propensities with epidemiological forecast models to construct a doubly robust estimation method that accounts for both measurement-error and selection bias. This method is then applied to estimate Indiana's active infection prevalence using case-count, hospitalization, and death data with demographic information, a statewide random molecular sample collected from April 25-29th, and Delphi's COVID-19 Trends and Impact Survey. We end with a series of recommendations based on the proposed methodology.

Identifying and Alleviating Bias Due to Differential Depletion of Susceptible People in Postmarketing Evaluations of COVID-19 Vaccines

Recent studies have provided key information about SARS-CoV-2 vaccines' efficacy and effectiveness (VE). One important question that remains is whether the protection conferred by vaccines wanes over time. However, estimates over time are subject to bias from differential depletion of susceptible individuals between vaccinated and unvaccinated groups. We examined the extent to which biases occur under different scenarios and assessed whether serological testing has the potential to correct this bias. By identifying nonvaccine antibodies, these tests could identify individuals with prior infection. We found that in scenarios with high baseline VE, differential depletion of susceptible individuals created minimal bias in VE estimates, suggesting that any observed declines are likely not due to spurious waning alone. However, if baseline VE was lower, the bias for leaky vaccines (which reduce individual probability of infection given contact) was larger and should be corrected for by excluding individuals with past infection if the mechanism is known to be leaky. Conducting analyses both unadjusted and adjusted for past infection could give lower and upper bounds for the true VE. Studies of VE should therefore enroll individuals regardless of prior infection history but also collect information, ideally through serological testing, on this critical variable.

Comparative Evaluation of Rapid Isothermal Amplification and Antigen Assays for Screening Testing of SARS-CoV-2

Human transmission of SARS-CoV-2 and emergent variants of concern continue to occur globally, despite mass vaccination campaigns. Public health strategies to reduce virus spread should therefore rely, in part, on frequent screening with rapid, inexpensive, and sensitive tests. We evaluated two digitally integrated rapid tests and assessed their performance using stored nasal swab specimens collected from individuals with or without COVID-19. An isothermal amplification assay combined with a lateral flow test had a limit of detection of 10 RNA copies per reaction, and a positive percent agreement (PPA)/negative percent agreement (NPA) during the asymptomatic and symptomatic phases of 100%/100% and 95.83/100%, respectively. Comparatively, an antigen-based lateral flow test had a limit of detection of 30,000 copies and a PPA/NPA during the asymptomatic and symptomatic phases of 82.86%/98.68% and 91.67/100%, respectively. Both the isothermal amplification and antigen-based lateral flow tests had optimized detection of SARS-CoV-2 during the peak period of transmission; however, the antigen-based test had reduced sensitivity in clinical samples with qPCR Ct values greater than 29.8. Low-cost, high-throughput screening enabled by isothermal amplification or antigen-based techniques have value for outbreak control.

Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics

Background

The impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown.

Methods

Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk change across groups.

Results

A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites.

Conclusions

Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection.

Funding

K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation. M.L. was supported by SeroNet cooperative agreement U01 CA261277.

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.