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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.

SUBMITTER: Zhang M

PROVIDER: S-EPMC6993983 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionBovine respiratory disease (BRD) has a significant impact on the health and welfare of dairy calves. It can result in increased antimicrobial usage, decreased growth rate and reduced future productivity. There is no gold standard antemortem diagnostic test for BRD in calves and no estimates of the prevalence of respiratory disease in seasonal calving dairy herds.MethodsTo estimate BRD prevalence in seasonal calving dairy herds in Ireland, 40 dairy farms were recruited and each farm was visited once during one of two calving seasons (spring 2020 & spring 2021). At that visit the prevalence of BRD in 20 calves between 4 and 6 weeks of age was determined using thoracic ultrasound score (≥3) and the Wisconsin respiratory scoring system (≥5). Hierarchical Bayesian latent class analysis was used to estimate the calf-level true prevalence of BRD, and the within-herd prevalence distribution, accounting for the imperfect nature of both diagnostic tests.ResultsIn total, 787 calves were examined, of which 58 (7.4%) had BRD as defined by a Wisconsin respiratory score ≥5 only, 37 (4.7%) had BRD as defined by a thoracic ultrasound score of ≥3 only and 14 (1.8%) calves had BRD based on both thoracic ultrasound and clinical scoring. The primary model assumed both tests were independent and used informed priors for test characteristics. Using this model the true prevalence of BRD was estimated as 4%, 95% Bayesian credible interval (BCI) (1%, 8%). This prevalence estimate is lower or similar to those found in other dairy production systems. Median within herd prevalence varied from 0 to 22%. The prevalence estimate was not sensitive to whether the model was constructed with the tests considered conditionally dependent or independent. When the case definition for thoracic ultrasound was changed to a score ≥2, the prevalence estimate increased to 15% (95% BCI: 6%, 27%).DiscussionThe prevalence of calf respiratory disease, however defined, was low, but highly variable, in these seasonal calving dairy herds.

Project description:AimsThe main focus of this study is to illustrate the importance of the statistical analysis in the evaluation of the accuracy of malaria diagnostic tests, without admitting a reference test, exploring a dataset (n=3317) collected in São Tomé and Príncipe.MethodsBayesian Latent Class Models (without and with constraints) are used to estimate the malaria infection prevalence, together with sensitivities, specificities, and predictive values of three diagnostic tests (RDT, Microscopy and PCR), in four subpopulations simultaneously based on a stratified analysis by age groups (< 5, ? 5 years old) and fever status (febrile, afebrile).ResultsIn the afebrile individuals with at least five years old, the posterior mean of the malaria infection prevalence is 3.2% with a highest posterior density interval of [2.3-4.1]. The other three subpopulations (febrile ? 5 years, afebrile or febrile children less than 5 years) present a higher prevalence around 10.3% [8.8-11.7]. In afebrile children under-five years old, the sensitivity of microscopy is 50.5% [37.7-63.2]. In children under-five, the estimated sensitivities/specificities of RDT are 95.4% [90.3-99.5]/93.8% [91.6-96.0]--afebrile--and 94.1% [87.5-99.4]/97.5% [95.5-99.3]--febrile. In individuals with at least five years old are 96.0% [91.5-99.7]/98.7% [98.1-99.2]--afebrile--and 97.9% [95.3-99.8]/97.7% [96.6-98.6]--febrile. The PCR yields the most reliable results in four subpopulations.ConclusionsThe utility of this RDT in the field seems to be relevant. However, in all subpopulations, data provide enough evidence to suggest caution with the positive predictive values of the RDT. Microscopy has poor sensitivity compared to the other tests, particularly, in the afebrile children less than 5 years. This type of findings reveals the danger of statistical analysis based on microscopy as a reference test. Bayesian Latent Class Models provide a powerful tool to evaluate malaria diagnostic tests, taking into account different groups of interest.

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

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