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Applications of time-series analysis to antibiotic resistance and consumption data

López-Lozano, J.-M., Monnet, D.L., Alonso, P.C., Quintero, A.C., Jiménez, N.G., Muñoz, A.Y., Thomas, C., Beyaert, A., Stevenson, M. and Riley, T.V. (2005) Applications of time-series analysis to antibiotic resistance and consumption data. In: Gould, I.M. and van der Meer, J.W.M., (eds.) Antibiotic Policies : Theory and Practice. Springer US, New York, pp. 447-463.

Link to Published Version: http://dx.doi.org/10.1007/0-387-22852-7_24
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Abstract

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

Publication Type: Book Chapter
Publisher: Springer US
Copyright: © 2005
Publishers Website: http://www.springer.com/gp/book/9780306485008
URI: http://researchrepository.murdoch.edu.au/id/eprint/35383
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