All statistical analyses were performed using Stata® v.16.1 (StataCorp LLC, College Station, TX, USA). To achieve normalized distributions, colony-forming units (CFU) derived quantities (CFU/g feces) were transformed to log base 10 (log10 CFU per gram of feces) for use as dependent variables in multi-level mixed effects linear regression. To determine the relative quantity of antibiotic-resistant log10 CFU per gram of feces to total log10 CFU per gram of feces, a new variable was created by subtracting the log10 CFU per gram of feces grown on antibiotic-supplemented agar from the log10 CFU per gram of feces of the corresponding plain agar plate. These differences were then also used as a dependent variable in multi-level mixed effects linear regression. A 3-way full-factorial model was constructed, factors being zinc (binary), menthol (binary) and sample day (2-level factor for Day 0 and Day 21). Full models were retained in all cases for biological reasons, regardless of the statistical significance of the interaction terms, necessitated because the treatments had not been applied before the Day 0 sampling was performed.

For statistical analysis of the phenotypic susceptibility of isolates, resistance to each antibiotic class (antibiotic class as defined by CLSI) was graphed by day and treatment group. The Gram-negative plate consisted of ten classes of antibiotics, and the Gram-positive plate consisted of 13 classes of antibiotics. Additionally, binary resistance to each class of antibiotic was summed for each isolate to create a new variable representing multi-drug resistance count (an integer variable), which also was graphed by day and treatment group. This variable was then used to determine multi-drug resistance as a binary variable (MDR, defined as resistance to ≥3 classes of antibiotics) for each isolate. A 3-way full factorial multi-level mixed effects logistic regression model was then used to determine the effect of sample day, zinc and/or menthol on the relative odds of MDR (a binary variable) for each of Gram-positive and Gram-negative bacteria. For each statistical model, marginal means were estimated and plotted by sample day 95% confidence intervals.

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