Arielle Beyaert

Antimicrobial drug use and methicillin-resistant Staphylococcus aureus, Aberdeen, 1996-2000.

Relationships between antimicrobial use and MRSA prevalence are analyzed in Aberdeen, Scotland.

Modelling the impact of antibiotic use on antibiotic-resistant Escherichia coli using population-based data from a large hospital and its surrounding community

OBJECTIVES To determine the temporal relationship between antibiotic use and incidence of antibiotic-resistant Escherichia coli in both the inpatient and outpatient setting of a large urban area. METHODS A retrospective observational time-series analysis was performed to evaluate the incidence of non-duplicate clinical isolates of E. coli resistant to ciprofloxacin, trimethoprim/sulfamethoxazole and cefepime from January 2000 through December 2007, combined with a transfer function model of aggregated data on antibiotic use in both settings obtained from the hospitals pharmacy and outpatient billing offices. RESULTS Ciprofloxacin resistance increased from 6.0% (2000) to 15.4% (2007; P<0.0001) and cefepime resistance from 0.9% (2002) to 3.2% (2007; P=0.01). Trimethoprim/sulfamethoxazole resistance remained stable (23.7%-25.8%). Total antibiotic use increased in both settings, while fluoroquinolone use increased significantly only among outpatients. A temporal effect between fluoroquinolone resistance in community E. coli isolates and outpatient use of ciprofloxacin (immediate effect and time lag 1 month) and moxifloxacin (time lag 4 months) was observed, explaining 51% of the variance over time. The incidence of cefepime resistance in E. coli was correlated with ciprofloxacin use in the inpatient (lag 1 month) and outpatient (lag 4 months) settings and with the use of ceftriaxone (lag 0 month), piperacillin/tazobactam (3 months) and cefepime (3 months) in the hospital (R2=51%). CONCLUSIONS These results support efforts to reduce prescribing of fluoroquinolones for control of resistant E. coli including extended-spectrum β-lactamase producers and show the added value of time-series analysis to better understand the interaction between community and hospital antibiotic prescribing and its spill-over effect on antibiotic resistance.

Markov-switching models, rational expectations and the term structure of interest rates

In order to evaluate the efficiency of the monetary transmission mechanism, we develop the formulas for testing rational expectations theory in the term structure of interest rates with VAR models of stochastically switching regimes in which all the parameters are regime dependent. These formulas are obtained for the strict version of rational expectations as well as for the case where measurement errors are assumed in the expectations relationship. They are extensible to other contexts that involve variables linked by rational-expectations behaviours. The testing procedure is implemented on interest rates of the Spanish inter-bank money market. Measurement errors must be assumed to find signs favourable to the theory.

Computation of the Beveridge–Nelson decomposition in the case of cointegrated systems with I(0) variables

Abstract This paper shows how the result of Arino and Newbold [Economics Letters, 61 (1998) 37] for the multivariate BN decomposition in the cointegrated case can be extended when I (0) variables are considered in the VEC model. An application of the extended procedure to the Spanish peseta/French franc real exchange rate is also provided.

Applications of Time-series Analysis to Antibiotic Resistance and Consumption Data

During the past 20 years, there have been numerous attempts to study the relationship between antimicrobial use and resistance using surveillance data. The method presented here represents another of those attempts. It proved helpful to demonstrate a temporal relationship between antimicrobial use and resistance and unlike most other methods,this method can take into account the use of several antimicrobials to explain specific types of resistance, quantify the effect of use on resistance,and estimate the delay between variations in use and subsequent variations in resistance.Additionally, it has allowed us to predict future levels of resistance based on past antimicrobial use and resistance data. Our observations show that ecologic systems such as that within the hospital tend to react to changes in antimicrobial use much faster than previously thought, that is, within a few months rather than several years. This finding has recently been confirmed by Corbella et al. who reported rapid variations in the percentage of imipenem-resistant Acinetobacter baumannii following changes in carbapenem use in a Spanish hospital (Corbella et al., 2000),and by Lepper et al. who also reported changes in P. aeruginosa resistance to imipenem following changes in hospital imipenem use (Lepper et al., 2002). The recent application of time-series analysis to antimicrobial use and resistance data from the primary healthcare sector in Denmark shows that this is probably also true outside hospitals (Monnet, 2000a). In conclusion, time-series analysis is a new tool that can help us make sense of antimicrobial use and resistance surveillance data,an area where modeling has proven difficult. Future developments must include confirmation of the usefulness of this method in other hospitals and in other countries.