Search

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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: In an effort to decrease transmission during the first years of the COVID-19 pandemic, public health officials encouraged masking, social distancing, and working from home, and restricted travel. However, many studies of the effectiveness of these measures had significant methodologic limitations. In this analysis, we used data from the TrackCOVID study, a longitudinal cohort study of a population-based sample of 3846 adults in the San Francisco Bay Area, to evaluate the association between self-reported protective behaviors and incidence of SARS-CoV-2 infection., Methods: Participants without SARS-CoV2 infection were enrolled from August to December 2020 and followed monthly with testing and surveys (median of four visits)., Results: A total of 118 incident infections occurred (3.0% of participants). At baseline, 80.0% reported always wearing a mask; 56.0% avoided contact with nonhousehold members some/most of the time; 9.6% traveled outside the state; and 16.0% worked 20 or more hours per week outside the home. Factors associated with incident infection included being Black or Latinx, having less than a college education, and having more household residents. The only behavioral factor associated with incident infection was working outside the home (adjusted hazard ratio 1.62, 95% confidence interval 1.02-2.59)., Conclusions: Focusing on protecting people who cannot work from home could help prevent infections during future waves of COVID-19, or future pandemics from respiratory viruses. This focus must be balanced with the known importance of directing resources toward those at risk of severe infections., Competing Interests: Declaration of Competing Interest The authors have no competing interests to disclose., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: We describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties., Methods: Using an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance., Results: We enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata., Conclusions: Our stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)