Estimation of multidrug resistance variability in the National Antimicrobial Monitoring System.

Multidrug resistance is a serious problem raising the specter of infections for which there is no treatment. One of the most important tools in combating multidrug resistance is large scale monitoring programs, because they track resistance over large geographic areas and time scales. This large scope, however, can also introduce variability into the data. The primary monitoring program in the United States is the National Antimicrobial Resistance Monitoring System (NARMS). This study examines the variability of a previously identified resistance pattern in Escherichia coli among ampicillin, gentamicin, sulfisoxazole, and tetracycline using samples isolated from chicken during the years 2004 to 2006 and 2008 to 2012. 2007 is excluded because sulfisozaxole resistance was not measured at slaughter that year. To assess variability in this resistance pattern susceptibility/resistance contingency tables were constructed for each of the 15 combinations of the 4 drugs for each of the years. For each table, variability across the years was assessed at the full table multinomial level as a measure of general variability of the resistance pattern and at the level of the highest order interaction term in a log-linear model of the table as a measure of variability in that particular component of the resistance pattern. A power analysis using the traditional asymptotic normal approximation and one using a Dirichlet-multinomial simulation were carried out to determine the effect of variation on ability to detect nonzero highest order loglinear model terms and the validity of the normal approximation in carrying out such tests. All tables exhibit overdispersion at the multinomial level and in their highest order model parameters. The normal approximation performs well for large sample sizes, low levels of dispersion, and small log-linear model parameters. The approximation breaks down as dispersion or the log linear model parameter grows or sample size shrinks. Taken together these analyses indicate that the level of variability in the NARMS dataset makes it difficult to detect multidrug resistance patterns at the current level of sample collection. In order to better control this dispersion NARMS could collect more variables on each of the samples.

Inferring the interaction structure of resistance to antimicrobials.

The growth of antimicrobial resistance presents a significant threat to human and animal health. Of particular concern is multi-drug resistance, as this increases the chances an infection will be untreatable by any antibiotic. In order to understand multi-drug resistance, it is essential to understand the association between drug resistances. Pairwise associations characterize the connectivity between resistances and are useful in making decisions about courses of treatment, or the design of drug cocktails. Higher-order associations, interactions, which tie together groups of drugs can suggest commonalities in resistance mechanism and lead to their identification. To capture interactions, we apply log-linear models of contingency tables to analyze publically available data on the resistance of Escheresia coli isolated from chicken and turkey meat by the National Antimicrobial Resistance Monitoring System. Standard large sample and conditional exact testing approaches for assessing significance of parameters in these models breakdown due to structured patterns inherent to antimicrobial resistance. To address this, we adopt a Bayesian approach which reveals that E. coli resistance associations can be broken into two subnetworks. The first subnetwork is characterized by a hierarchy of ß-lactams which is consistent across the chicken and turkey datasets. Tier one in this hierarchy is a near equivalency between amoxicillin-clavulanic acid, ceftriaxone and cefoxitin. Susceptibility to tier one then implies susceptibility to ceftiofur. The second subnetwork is characterized by more complex interactions between a variety of drug classes that vary between the chicken and turkey datasets.

Monitoring Antimicrobial Resistance in the Food Supply Chain and Its Implications for FDA Policy Initiatives.

In response to concerning increases in antimicrobial resistance (AMR), the Food and Drug Administration (FDA) has decided to increase veterinary oversight requirements for antimicrobials and restrict their use in growth promotion. Given the high stakes of this policy for the food supply, economy, and human and veterinary health, it is important to rigorously assess the effects of this policy. We have undertaken a detailed analysis of data provided by the National Antimicrobial Resistance Monitoring System (NARMS). We examined the trends in both AMR proportion and MIC between 2004 and 2012 at slaughter and retail stages. We investigated the makeup of variation in these data and estimated the sample and effect size requirements necessary to distinguish an effect of the policy change. Finally, we applied our approach to take a detailed look at the 2005 withdrawal of approval for the fluoroquinolone enrofloxacin in poultry water. Slaughter and retail showed similar trends. Both AMR proportion and MIC were valuable in assessing AMR, capturing different information. Most variation was within years, not between years, and accounting for geographic location explained little additional variation. At current rates of data collection, a 1-fold change in MIC should be detectable in 5 years and a 6% decrease in percent resistance could be detected in 6 years following establishment of a new resistance rate. Analysis of the enrofloxacin policy change showed the complexities of the AMR policy with no statistically significant change in resistance of both Campylobacter jejuni and Campylobacter coli to ciprofloxacin, another second-generation fluoroquinolone.