Classification Tree Method for Bacterial Source Tracking with Antibiotic Resistance Analysis Data

Abstract

Various statistical classification methods, including discriminant analysis, logistic regression, and cluster analysis, have been used with antibiotic resistance analysis (ARA) data to construct models for bacterial source tracking (BST). We applied the statistical method known as classification trees to build a model for BST for the Anacostia Watershed in Maryland. Classification trees have more flexibility than other statistical classification approaches based on standard statistical methods to accommodate complex interactions among ARA variables. This article describes the use of classification trees for BST and includes discussion of its principal parameters and features. Anacostia Watershed ARA data are used to illustrate the application of classification trees, and we report the BST results for the watershed.Bacterial source tracking (BST) with antibiotic resistance analysis (ARA) has been conducted using various statistical classification models to identify sources of bacterial contamination of surface waters (3-5, 9-15). Isolates are obtained from fecal samples from known sources, such as humans, pets, livestock, and wildlife, and are tested for antibiotic resistance against a panel of antibiotics at different concentrations. The isolates comprise a reference library that is used for developing a statistical classification model to predict probable bacterial sources based on antibiotic resistance profiles. The model is then applied to classify unknown water sample isolates treated with the same antibiotics. The result is a frequency distribution of water sample isolates by source, which is used to estimate the relative contributions of the sources to bacterial contamination in the watershed.Statistical classification methods, including discriminant analysis, logistic regression, and cluster analysis, have been used to develop classification models for ARA data (3)(4)(5)14). Classification trees provide an alternative statistical approach for this BST method. Classification tree analysis methods have the flexibility to accommodate complex interactions among antibiotic variables (1, 6). This article describes and discusses the use of classification trees for BST. To aid the presentation, we describe an application of classification tree modeling to data collected from the Anacostia River Watershed in Maryland. (Our model was used to develop a risk management program for bacterial contamination of the Anacostia River [8].) MATERIALS AND METHODSARA data. We use ARA data collected from the Anacostia Watershed to aid our description and discussion of the classification tree approach for BST. The Anacostia Watershed reference library was developed from 90 scat samples collected from three general sources in Maryland's Anacostia Watershed (pets, livestock, and wildlife) and 50 human source samples from sewage treatment facilities. A total of 1,155 known source enterococcal isolates were obtained from these fecal samples. Table 1 shows the distribution of samples and isolates among the four sources. Forty-two ant...

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