Adjusting COVID-19 Seroprevalence Survey Results to Account for Test Sensitivity and Specificity

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

COVID-19: seroprevalence and adherence to preventive measures in Arkhangelsk, Northwest Russia

BACKGROUND

The published estimates of SARS-CoV-2 seroprevalence in Russia are few. The study aimed to assess the SARS-CoV-2 seroprevalence in Arkhangelsk (Northwest Russia), in a year after the start of the pandemic, to evaluate the population adherence to non-pharmaceutical interventions (NPIs), and to investigate characteristics associated with COVID-19 seropositive status.

METHODS

We conducted a SARS-CoV-2 seroprevalence study between 24 February and 30 June 2021 involving 1332 adults aged 40-74 years. Logistic regression models were fit to identify factors associated with seropositive status and with adherence to NPIs.

RESULTS

Less than half (48.9%) of study participants adhered all recommended NPIs. Male sex (odds ratio [OR] 1.7, 95% confidence intervals [CI] 1.3; 2.3), regular employment (OR 1.8, 95% CI 1.3; 2.5) and low confidence in the efficiency of the NPIs (OR 1.9, 95% CI 1.5; 2.5) were associated with low adherence to internationally recommended NPIs. The SARS-CoV-2 seroprevalence rate was 65.1% (95% CI: 62.5; 67.6) and increased to 73.0% (95% CI: 67.1; 85.7) after adjustment for test performance. Regular employment (OR 2.0, 95% CI 1.5; 2.8) and current smoking (OR 0.4, 95% CI 0.2; 0.5) were associated with being seropositive due to the infection.

CONCLUSIONS

Two third of the study population were seropositive in a year after the onset of the pandemic in Arkhangelsk. Individuals with infection-acquired immunity were more likely to have regular work and less likely to be smokers. The adherence to NPIs was not found associated with getting the virus during the first year of the pandemic.

When Does Differential Outcome Misclassification Matter for Estimating Prevalence?

BACKGROUND

When accounting for misclassification, investigators make assumptions about whether misclassification is "differential" or "nondifferential." Most guidance on differential misclassification considers settings where outcome misclassification varies across levels of exposure, or vice versa. Here, we examine when covariate-differential misclassification must be considered when estimating overall outcome prevalence.

METHODS

We generated datasets with outcome misclassification under five data generating mechanisms. In each, we estimated prevalence using estimators that (a) ignored misclassification, (b) assumed misclassification was nondifferential, and (c) allowed misclassification to vary across levels of a covariate. We compared bias and precision in estimated prevalence in the study sample and an external target population using different sources of validation data to account for misclassification. We illustrated use of each approach to estimate HIV prevalence using self-reported HIV status among people in East Africa cross-border areas.

RESULTS

The estimator that allowed misclassification to vary across levels of the covariate produced results with little bias for both populations in all scenarios but had higher variability when the validation study contained sparse strata. Estimators that assumed nondifferential misclassification produced results with little bias when the covariate distribution in the validation data matched the covariate distribution in the target population; otherwise estimates assuming nondifferential misclassification were biased.

CONCLUSIONS

If validation data are a simple random sample from the target population, assuming nondifferential outcome misclassification will yield prevalence estimates with little bias regardless of whether misclassification varies across covariates. Otherwise, obtaining valid prevalence estimates requires incorporating covariates into the estimators used to account for misclassification.

Seroprevalence of COVID-19 infection in a densely populated district in the eastern Democratic Republic of Congo

Data on coronavirus disease 2019 (COVID-19) prevalence in the Democratic Republic of Congo are scarce. We conducted a cross-sectional study to determine the seroprevalence of antibodies against anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the slum of Kadutu, city of Bukavu, between June and September 2021. The survey participants were all unvaccinated against SARS-CoV-2. The crude seroprevalence rate was adjusted to the known characteristics of the assay. Participants aged 15-49 years old made up 80% of the population enrolled in the study (n = 507; 319 women and 188 men). The overall crude and adjusted seroprevalence rates of antibodies for COVID-19 were 59.7% (95% CI 55.4-63.9%) and 84.0% (95% CI 76.2-92.4%), respectively. This seroprevalence rate indicates widespread dissemination of SARS-CoV-2 in these communities. COVID-19 symptoms were either absent or mild in more than half of the participants with antibodies for COVID-19 and none of the participants with antibodies for COVID-19 required hospitalisation. These results suggest that SARS-CoV-2 spread did not appear to be associated with severe symptoms in the population of these settlements and that many cases went unreported in these densely populated locations. The relevance of vaccination in these communities should be thoroughly investigated.