Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

Abstract

Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model.

Regression models for count data with excess zeros: A comparison using survey data

“…The Quantitative Methods for Psychology 8 contains both structural and sampling zeros, either of zero inflated and hurdle regression models can be used. According to Hüls et al (2017), the hurdle models can handle all type of zeros identically by definition and they never discriminate them based on their origin. The zero inflated models has the advantage of being able to differentiate zeros as structural and sampling and the hurdle models never discriminate zeros based on the origin.…”

Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review

“…The search strategy will be tested and refined to ensure its sensitivity and specificity. Previously identified papers will be used to test the effectiveness of the search strategy 28–42…”

“…Previously identified papers will be used to test the effectiveness of the search strategy. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] The study will focus on the records describing the development and dissemination of bacterial AMR (problem) in all three One Health compartments: humans, animals and environments (context), using mathematical and statistical modelling (interest). Hence, various variants of the keywords (model, bacteria, antibiotics and resistance) will be used (table 1).…”

Origin Sample Prediction and Spatial Modeling of Antimicrobial Resistance in Metagenomic Sequencing Data

“…A number of bioinformatics methods and tools exist to analyze such data and discover AMR mechanisms (Lal Gupta et al, 2020 ; Van Camp et al, 2020 ). Such mechanisms are subject of intensive research studies which include negative binomial, quasi-Poisson, Zero-inflated models (Hüls et al, 2017 ). The International Metagenomics and Metadesign of Subways and Urban Biomes (MetaSUB) Consortium is a multidisciplinary initiative with participation of a large number of researchers in different fields who develop and apply metagenomic methods for sample collection, DNA/RNA isolation, taxa characterization, and data visualization (Mason et al, 2016 ).…”