Confidence interval methods for antimicrobial resistance surveillance data.

ABSTRACT:

Background

Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents.

Methods

We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume dat

SUBMITTER: Kalanxhi E 

PROVIDER: S-EPMC8191092 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature