Bayesian estimation of the prevalence of antimicrobial resistance: a mathematical modelling study.

ABSTRACT:

Background

Estimates of the prevalence of antimicrobial resistance (AMR) underpin effective antimicrobial stewardship, infection prevention and control, and optimal deployment of antimicrobial agents. Typically, the prevalence of AMR is determined from real-world antimicrobial susceptibility data that are time delimited, sparse, and often biased, potentially resulting in harmful and wasteful decision-making. Frequentist methods are resource intensive because they rely on large datasets.

Objectives

To determine whether a Bayesian approach could present a more reliable and more resource-efficient way to estimate population prevalence of AMR than traditional frequentist methods.

Methods

Retrospectively collected, open-source, real-world pseudonymized healthcare data were

SUBMITTER: Howard A 

PROVIDER: S-EPMC11368424 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature