A Bayesian latent class mixture model with censoring for correlation analysis in antimicrobial resistance across populations

• Published in: BMC medical research methodology Vol. 21; no. 1; pp. 1 - 12
• Main Authors: Zhang, Min, Wang, Chong, O’Connor, Annette M.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 20.09.2021; BMC
• Subjects: Animal populations; Antibiotics; Antimicrobial agents; Antimicrobial resistance; Bacteria; Bayesian latent class model; Censorship; Correlation; Correlation analysis; Data analysis; Drug resistance; Health Sciences; Health surveillance; Latent class analysis; Medical research; Medicine; Medicine & Public Health; Minimum inhibitory concentration; Monitoring systems; NARMS; Public health; Research Article; Salmonella; Statistical Theory and Methods; statistics and modelling; Statistics for Life Sciences; Theory of Medicine/Bioethics; United States > US; Correlation; Bayesian latent class model; Antimicrobial resistance; NARMS; Minimum inhibitory concentration
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-021-01384-w

Background The emergence of antimicrobial resistance across populations is a global threat to public health. Surveillance programs often monitor human and animal populations to evaluate trends of emergence in these populations. Many national level antibiotic resistance surveillance programs quantify the proportion of resistant bacteria as a means of monitoring emergence and control measures. The reason for monitoring these different populations are many, including interest in similar changes in resistance which might provide insight into emergence and control options. Methods In this research, we developed a method to quantify the correlation in antimicrobial resistance across populations, for the conventionally unnoticed mean shift of the susceptible bacteria. With the proposed Bayesian latent class mixture model with censoring and multivariate normal hierarchy, we address several challenges associated with analyzing the minimum inhibitory concentration data. Results Application of this approach to the surveillance data from National Antimicrobial Resistance Monitoring System led to a detection of positive correlation in the central tendency of azithromycin resistance of the susceptible populations from Salmonella serotype Typhimurium across food animal and human populations. Conclusions Our proposed approach has been shown to be accurate and superior to the commonly used naïve estimation by simulation studies. Further implementation of this Bayesian model could serve as a useful tool to indicate the co-existence of antimicrobial resistance, and potentially a need of clinical intervention.