Estimation of SARS-CoV-2 Seroprevalence in Central North Carolina: Accounting for Outcome Misclassification in Complex Sample Designs.

ABSTRACT:

Background

Population-based seroprevalence studies are crucial to understand community transmission of COVID-19 and guide responses to the pandemic. Seroprevalence is typically measured from diagnostic tests with imperfect sensitivity and specificity. Failing to account for measurement error can lead to biased estimates of seroprevalence. Methods to adjust seroprevalence estimates for the sensitivity and specificity of the diagnostic test have largely focused on estimation in the context of convenience sampling. Many existing methods are inappropriate when data are collected using a complex sample design.

Methods

We present methods for seroprevalence point estimation and confidence interval construction that account for imperfect test performance for use with complex sample

SUBMITTER: Vias NP 

PROVIDER: S-EPMC10403265 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature