A Hierarchical Bayesian Latent Class Mixture Model with Censorship for Detection of Linear Temporal Changes in Antibiotic Resistance

• Activity Number: 171 - SPAAC Poster Competition
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Min Zhang* and Chong Wang and Annette O'Connor
• Companies: Iowa State University and Iowa State University and Iowa State University
• Keywords: Antimicrobial resistance; Minimum inhibitory concentration; Hierarchical Bayesian linear model; Latent class; Salmonella enterica I,4,[5],12:i:-; Chloramphenicol/ Ceftiofur

Abstract

Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high priority for researchers and public health officials. Currently, detection of temporal changes in AMR relies mainly on analysis of the proportion of resistant isolates based on the dichotomization of minimum inhibitory concentration (MIC) values. In our work, we developed a hierarchical Bayesian latent class mixture model that incorporates a linear trend for the resistance of the non-resistant population. By introducing latent variables, our model addressed the challenges associated with the AMR MIC values, compensating for the censored nature of the MIC observations as well as the mixed components indicated by the censored MIC distributions. Inclusion of linear regression with time as a covariate in the hierarchical structure allowed modelling of the linear creep of the mean resistance. By exploring the patterns of the mean resistance level, implementation of our approach to the analysis of AMR MIC datasets could serve as a timely indicator of a need for antibiotic intervention before an outbreak of resistance, highlighting the relevance of this work for public health.