A hierarchical Bayesian latent class mixture model with censorship for detection of linear temporal changes in antibiotic resistance

Abstract

Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high priority for researchers and public health officials. One critical component of this control effort is timely detection of emerging or increasing resistance using surveillance programs. 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 mean log2MIC 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 log2MIC in the non-resistant population. The hierarchical Bayesian model was accurate and robust as assessed in simulation studies. The proposed approach was illustrated using Salmonella enterica I,4,[5],12:i:- treated with chloramphenicol and ceftiofur in human and veterinary samples, revealing some significant linearly increasing patterns from the applications. Implementation of our approach to the analysis of an AMR MIC dataset would provide surveillance programs with a more complete picture of the changes in AMR over years by exploring the patterns of the mean resistance level in the non-resistant population. Our model could therefore 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. Currently, however, due to extreme right censoring on the MIC data, this approach has limited utility for tracking changes in the resistant population.

Fig 1. Conversion plot between observed and latent log₂MIC.

In this example, the serial dilution experiment starts at MIC = 2⁻² = 0.25 and ends at MIC = 2⁴ = 16.

Fig 2. Estimation results for Salmonella enterica I,4,[5],12:i:- tested with CHL in the CDC NARMS dataset.

The grey bars represent the observed proportions of resistant bacteria (left y-axis). The grey line indicates the naïve mean of log₂MIC in the non-resistant population. The red line connects the point estimates of the mean log₂MIC in the non-resistant population. The blue line represents the estimated linear trend, shaded with its 95% CI.

Fig 3. Estimation results for Salmonella enterica I,4,[5],12:i:- tested with TIO in the CDC NARMS dataset.

The grey bars represent the observed proportions of resistant bacteria (left y-axis). The grey line indicates the naïve mean of log₂MIC in the non-resistant population. The red line connects the point estimates of the mean log₂MIC in the non-resistant population. The blue line represents the estimated linear trend, shaded with its 95% CI.

Fig 4. Estimation results for Salmonella enterica I,4,[5],12:i:- tested with TIO in the ISU VDL dataset.

The grey bars represent the observed proportions of resistant bacteria (left y-axis). The grey line indicates the naïve mean of log₂MIC in the non-resistant population. The red line connects the point estimates of the mean log₂MIC in the non-resistant population. The blue line represents the estimated linear trend, shaded with its 95% CI.