Covariate-adjusted generalized pairwise comparisons in small samples.

Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds.

Patterns of antimicrobial resistance in Salmonella isolates from fattening pigs in Spain.

BACKGROUND: Swine are considered a major source of foodborne salmonellosis, a public health issue further complicated by the circulation of multidrug-resistant Salmonella strains that threaten the safety of the food chain. The current study aimed to identify patterns that can help to understand the epidemiology of antimicrobial resistance (AMR) in Salmonella in pigs in Spain through the application of several multivariate statistical methods to data from the AMR national surveillance programs from 2001 to 2017. RESULTS: A total of 1,318 pig Salmonella isolates belonging to 63 different serotypes were isolated and their AMR profiles were determined. Tetracycline resistance across provinces in Spain was the highest among all antimicrobials and ranged from 66.7% to 95.8%, followed by sulfamethoxazole resistance (range: 42.5% - 77.8%), streptomycin resistance (range: 45.7% - 76.7%), ampicillin resistance (range: 24.3% - 66.7%, with a lower percentage of resistance in the South-East of Spain), and chloramphenicol resistance (range: 8.5% - 41.1%). A significant increase in the percentage of resistant isolates to chloramphenicol, sulfamethoxazole, ampicillin and trimethoprim from 2013 to 2017 was observed. Bayesian network analysis showed the existence of dependencies between resistance to antimicrobials of the same but also different families, with chloramphenicol and sulfamethoxazole in the centre of the networks. In the networks, the conditional probability for an isolate susceptible to ciprofloxacin that was also susceptible to nalidixic acid was 0.999 but for an isolate resistant to ciprofloxacin that was also resistant to nalidixic acid was only 0.779. An isolate susceptible to florfenicol would be expected to be susceptible to chloramphenicol, whereas an isolate resistant to chloramphenicol had a conditional probability of being resistant to florfenicol at only 0.221. Hierarchical clustering further demonstrated the linkage between certain resistances (and serotypes). For example, a higher likelihood of multidrug-resistance in isolates belonging to 1,4,[5],12:i:- serotype was found, and in the cluster where all isolates were resistant to tetracycline, chloramphenicol and florfenicol, 86.9% (n = 53) of the isolates were Typhimurium. CONCLUSION: Our study demonstrated the power of multivariate statistical methods in discovering trends and patterns of AMR and found the existence of serotype-specific AMR patterns for serotypes of public health concern in Salmonella isolates in pigs in Spain.

Time trend of prevalence and susceptibility to nitrofurantoin of urinary MDR Escherichia coli from outpatients.

OBJECTIVES: To assess the time trend of the prevalence of urinary MDR Escherichia coli in Belgian outpatients (2005 versus 2011-12), the antibiotic susceptibility of urinary MDR E. coli, and the time trend of non-susceptibility to nitrofurantoin, i.e. first-line treatment for uncomplicated urinary tract infections (UTIs), of urinary MDR E. coli (2005 versus 2011-12). METHODS: In this secondary analysis of a multicentre study, which collected a convenience sample of voluntary participating laboratories (15 and 8 in 2005 and 2011-12, respectively), we analysed antimicrobial susceptibilities (ampicillin, amoxicillin/clavulanate, cefalotin, ciprofloxacin, nitrofurantoin and trimethoprim/sulfamethoxazole) of urinary E. coli. MDR was defined as resistance to three or more of these agents. The prevalence of MDR E. coli and its non-susceptibility to nitrofurantoin was compared between 2005 and 2011-12 using a generalized estimating equation model. RESULTS: MDR status could be determined for 9704 and 12512 urinary E. coli isolates from 7911 and 9441 patients in 2005 and 2011-12, respectively, with most patients being women (79% in both study periods). The prevalence of MDR increased from 28.4% (2758/9704) in 2005 to 34.3% (4286/12512) in 2011-12 (adjusted OR 1.305; 95% CI 1.220-1.397). Within the MDR isolates, the prevalence of nitrofurantoin non-susceptibility decreased from 23.2% (623/2684) in 2005 to 10.7% (455/4253) in 2011-12 (adjusted OR 0.424; 95% CI 0.363-0.494). CONCLUSIONS: Despite a high prevalence of MDR E. coli in urinary samples from Belgian outpatients, nitrofurantoin could still be recommended as first-line empirical treatment in uncomplicated UTIs.

Bayesian estimation of multivariate normal mixtures with covariate-dependent mixing weights, with an application in antimicrobial resistance monitoring.

Bacteria with a reduced susceptibility against antimicrobials pose a major threat to public health. Therefore, large programs have been set up to collect minimum inhibition concentration (MIC) values. These values can be used to monitor the distribution of the nonsusceptible isolates in the general population. Data are collected within several countries and over a number of years. In addition, the sampled bacterial isolates were not tested for susceptibility against one antimicrobial, but rather against an entire range of substances. Interest is therefore in the analysis of the joint distribution of MIC data on two or more antimicrobials, while accounting for a possible effect of covariates. In this regard, we present a Bayesian semiparametric density estimation routine, based on multivariate Gaussian mixtures. The mixing weights are allowed to depend on certain covariates, thereby allowing the user to detect certain changes over, for example, time. The new approach was applied to data collected in Europe in 2010, 2012, and 2013. We investigated the susceptibility of Escherichia coli isolates against ampicillin and trimethoprim, where we found that there seems to be a significant increase in the proportion of nonsusceptible isolates. In addition, a simulation study was carried out, showing the promising behavior of the proposed method in the field of antimicrobial resistance.

Application of the Vertex Exchange Method to estimate a semi-parametric mixture model for the MIC density of Escherichia coli isolates tested for susceptibility against ampicillin.

In the last decades, considerable attention has been paid to the collection of antimicrobial resistance data, with the aim of monitoring non-wild-type isolates. This monitoring is performed based on minimum inhibition concentration (MIC) values, which are collected through dilution experiments. We present a semi-parametric mixture model to estimate the entire MIC density on the continuous scale. The parametric first component is extended with a non-parametric second component and a new back-fitting algorithm, based on the Vertex Exchange Method, is proposed. Our data example shows how to estimate the MIC density for Escherichia coli tested for ampicillin and how to use this estimate for model-based classification. A simulation study was performed, showing the promising behavior of the new method, both in terms of density estimation as well as classification.

Estimation of the wild-type minimum inhibitory concentration value distribution.

Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non-wild-type isolates has therefore gained increased interest. Monitoring is performed based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. In order to account for the unobserved population heterogeneity of wild-type and non-wild-type isolates, mixture models are extremely useful. Instead of estimating the entire mixture globally, it was our major aim to provide an estimate for the wild-type first component only. The characteristics of this first component are not expected to change over time, once the wild-type population has been confidently identified for a given antimicrobial. With this purpose, we developed a new method based on the multinomial distribution, and we carry out a simulation study to study the properties of the new estimator. Because the new approach fits within the likelihood framework, we can compare distinct distributional assumptions in order to determine the most suitable distribution for the wild-type population. We determine the optimal parameters based on the AIC criterion, and attention is also paid to the model-averaged approach using the Akaike weights. The latter is thought to be very suitable to derive specific characteristics of the wild-type distribution and to determine limits for the wild-type MIC range. In this way, the new method provides an elegant means to compare distinct distributional assumptions and to quantify the wild-type MIC distribution of specific antibiotic-bacterium combinations.

A multicategory logit model detecting temporal changes in antimicrobial resistance.

Monitoring and investigating temporal trends in antimicrobial data is a high priority for human and animal health authorities. Timely detection of temporal changes in antimicrobial resistance (AMR) can rely not only on monitoring and analyzing the proportion of resistant isolates based on the use of a clinical or epidemiological cut-off value, but also on more subtle changes and trends in the full distribution of minimum inhibitory concentration (MIC) values. The nature of the MIC distribution is categorical and ordinal (discrete). In this contribution, we developed a particular family of multicategory logit models for estimating and modelling MIC distributions over time. It allows the detection of a multitude of temporal trends in the full discrete distribution, without any assumption on the underlying continuous distribution for the MIC values. The experimental ranges of the serial dilution experiments may vary across laboratories and over time. The proposed categorical model allows to estimate the MIC distribution over the maximal range of the observed experiments, and allows the observed ranges to vary across labs and over time. The use and performance of the model is illustrated with two datasets on AMR in Salmonella.

A trend analysis of antimicrobial resistance in commensal Escherichia coli from several livestock species in Belgium (2011-2014).

A temporal trend analysis was performed on antimicrobial resistance data collected over 4 consecutive years (2011-2014) in the official Belgian antimicrobial resistance monitoring programme. Commensal Escherichia coli strains were isolated from faecal samples of four livestock categories (veal calves, young beef cattle, broiler chickens and slaughter pigs) and the trends of resistance profiles were analysed. The resistance prevalence remained high (>50%) during the study period for ampicillin in veal calves and chickens, for ciprofloxacin and nalidixic acid in chickens, for sulfamethoxazole in veal calves, chickens and pigs and for tetracycline in veal calves. Using logistic regression and Generalized Estimating Equation and after p value adjustment for multiple testing (Linear step-up method), statistically significant decreasing temporal trends were observed for several of the 11 tested antimicrobials in several livestock categories: in veal calves (10/11), in chickens (6/11) and in pigs (5/11). A significant increasing trend was observed for the prevalence of resistance to ciprofloxacin in chickens. Multi-resistance, considered as the resistance to at least three antimicrobials of different antibiotic classes, was observed in the four livestock categories but was significantly decreasing in veal calves, chickens and pigs. Overall, the prevalence of resistance and of multi-resistance was lowest in the beef cattle livestock category and highest in broiler chickens. These decreasing temporal trends of antimicrobial resistance might be due to a decrease of the total antimicrobial consumption for veterinary use in Belgium which was reported for the period between 2010 and 2013. The methodology and statistical tools developed in this study provide outputs which can detect shifts in resistance levels or resistance trends associated with particular antimicrobial classes and livestock categories. Such outputs can be used as objective evidence to evaluate the possible efficacy of measures taken by animal health authorities and stakeholders in the livestock sector to limit antimicrobial resistance occurrence.