Conclusion
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.
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References
Allaouchiche, B., Monnet, D. L., López-Lozano, J. M. et al., 2002a, Monthly incidence of ICU-acquired Candida sp. infections and its relationship to consumption of specific antibiotics in a French ICU: A time series analysis. 42nd Interscience Conference on Antimicrobial Agents and Chemotherapy, San Diego, CA, 27–30 September.
Allaouchiche, B., Monnet, D. L., López-Lozano, J. M. et al., 2002b, Incidence of ICU-acquired infections due to antimicrobial-resistant microorganisms and relationship with antimicrobial use, infection control and workload in a French ICU. 42nd Interscience Conference on Antimicrobial Agents and Chemotherapy, San Diego, CA, 27–30 September.
Box, G. E. P. and Jenkins, G. M., 1976, Time Series Analysis: Forecasting and Control, 2nd edition. Holden Day, San Francisco, CA.
Campillos, P., Tarazona, M. V., Burgos, A., Aznar, J., González, M., Alós, M. et al., 2002, Distribution, trends and seasonal behaviour of antimicrobials used in five Spanish sanitary districts. 12th ECCMID, Milano (I), 24–27 April, 2002, abstr. O231. Clin. Microbiol. Infec., 8(Suppl. 1).
Corbella, X., Montero, A., Pujol, M. et al., 2000, Emergence and rapid spread of carbapenem resistance during a large and sustained hospital outbreak of multiresistant Acinetobacter baumannii. Clin. Microbiol., 38, 4086–4095.
Crabtree, B. F., Ray, S. C., Scmidt, P. M., O’Connor, P. J., and Schmidt, D. D., 1990, The individual over time: Time series applications in health care research. J. Clin. Epidemiol., 43, 241–260.
Goossens, H., Ghysels, G., Van Laethem, Y. et al., 1986, Predicting gentamicin resistance from annual usage in hospital. Lancet, 2, 804–805.
Helfenstein, U., 1996, Box-Jenkins modelling in medical research. Stat. Meth. Med. Res., 5, 3–22.
Lepper, P. M., Grusa, E., Reichl, H., Hogel, J., and Trautmann, M., 2002, Consumption of imipenem correlates with beta-lactam resistance in Pseudomonas aeruginosa. Antimicrob. Agents Chemother., 46(9), 2920–2925.
López-Lozano, J. M., Burgos, A., Gould, I. M., Campillos, P., MacKenzie, F. M., Monnet, D. L. et al., 2002a, Trends and seasonal behaviour of community antimicrobial use in three European countries. 42nd Interscience Conference on Antimicrobial Agents and Chemotherapy, San Diego, CA, 27–30 September. Abstract No. O-1004.
López-Lozano, J. M., Monnet, D. L., Burgos Sanjose, A., Gonzalo, N., Campillos, P., and Yagüe, A., 2000a, Modelling the temporal relationship between use of amikacin and other antimicrobials and amikacin resistance in Pseudomonas aeruginosa isolates: A time series analysis. 3rd European Congress of Chemotherapy, Madrid, 7–10 May 2000, abstr. T127. Revista Española de Quimioterapia-Spanish J. Chemother. 13(Suppl. 2), 65.
López-Lozano, J. M., Monnet, D. L., Yagüe, A., Burgos, A., Gonzalo, N., Campillos, P. et al., 2000b, Modelling and forecasting antimicrobial resistance and its dynamic relationship to antimicrobial use: A time series analysis. Int. J. Antimicrob. Agents, 14, 21–31.
López-Lozano, J. M., Monnet, D. L., Yagüe, A., Campillos, P., Gonzalo, N., and Burgos, A., 2002b, Surveillance de la résistance bactérienne et modélisation de sa relation avec les consommations d’antibiotiques au moyen de l’analyse des séries chronologiques. Bull. Soc. Fr. Microbiol. 17.
López-Lozano, J. M., Rodríguez, J. C., Sirvent, E., González, M., Royo, G., and Cabrera, A., 2003, Comparison of several criteria for rejecting multiple isolations on Pseudomonas aeruginosa antimicrobial sensitivity estimation. 13th European Congress of Clinical Microbiology and Infectious Diseases. Glasgow (UK). May. Abstract No. P728.
Ludlam, H., Brown, N., Sule, O., Redpath, C., Coni, N., and Owen, G., 1999, An antibiotic policy associated with reduced risk of Clostridium difficile-associated diarrhoea. Age and Ageing, 28, 578–580.
MacKenzie, F. M., López-Lozano, J. M., Beyaert, A., Monnet, D. L., Camacho, M., Stuart, D. et al., 2002a, Modelling the temporal relationship between hospital use of macrolides, third generation cephalosporins and fluoroquinolones, and the percentage of methicillin-resistant Staphylococcus aureus isolates: A time series analysis. 12th ECCMID, Milano (I), 24–27 April 2002, Abstract O231. Clin. Microbiol. Infect. 8(Suppl. 1), 32.
MacKenzie, F. M., López-Lozano, J. M., Gould I. M, Wilson, R., Beyaert, A., Camacho, M. et al., 2002b, Temporal relationship between prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in one hospital and prevalence of MRSA in the surrounding community: A time series analysis. 42nd Interscience Conference on Antimicrobial Agents and Chemotherapy, San Diego (CA, USA), 27–30 September. Abstract No. K-103.
MacKenzie, F. M., López-Lozano, J. M., Monnet, D. L., Camacho, M., Wilson, R., Stuart, D. et al., 2003, Relationship between methicillin-resistant S. aureus outbreaks in two Scottish hospitals. 13th ECCMID, Glasgow (UK), 10–12 May 2003, Abstract O338. Clin. Microbiol. Infect., 8(Suppl. 1), 32.
McGowan, J. E. Jr., 1987, Is antimicrobial resistance in hospitals related to antibiotic use? Bull. N.Y. Acad. Med., 63, 253–268.
McGowan, J. E. Jr., 1994, Do intensive hospital antibiotic control programs prevent the spread of antibiotic resistance? Infect. Control. Hosp. Epidemiol., 15, 478–483.
Monnet, D. L., 2000a, Antimicrobial use and resistance in Denmark. In: Abstracts of the 40th Interscience Conference on Antimicrobial Agents and Chemotherapy; 2000 September 17–20; Toronto, Canada. Washington, DC: American Society for Microbiology, 2000: p. 527.
Monnet, D. L., 2000b, Toward multinational antimicrobial resistance surveillance systems in Europe. Int. J. Antimicrob. Agents, 15, 91–101.
Monnet, D. L., Archibald, L. K., Phillips, L., Tenover, F. C., McGowan, J. E. Jr., and Gaynes, R. P., 1998, Antimicrobial use and resistance in eight US hospitals: Complexities of analysis and modeling. Intensive Care Antimicrobial Resistance Epidemiology Project and National Nosocomial Infections Surveillance System Hospitals. Infect. Control Hosp. Epidemiol., 19(6), 388–394.
Monnet, D. L., Lopez-Lozano, J. M., Campillos, P., Burgos, A., Yague, A., and Gonzalo, N., 2001, Making sense of antimicrobial use and resistance surveillance data: Application of ARIMA and transfer function models. Clin. Microbiol. Infect., 7(Suppl. 5), 29–36.
Monnet, D. L., Sørensen, T. L., Johansen, H. L., and López-Lozano, J. M., 1999, Impact of a Reimbursement Policy on Antimicrobial Prescriptions in the Primary Health Care Sector, Denmark, 1994–1998: A Time-Series Analysis. In: Abstracts of the 39th Interscience Conference on Antimicrobial Agents and Chemotherapy, San Francisco, CA. Washington, DC: American Society for Microbiology, 1999: p. 733.
Saez, M., Sunyer, J., Castellsague, J., Murillo, C., and Antó, J. M., 1995, Relationship between weather temperature and mortality: A time series analysis approach in Barcelona. Int. J. Epidemiol., 24, 576–582.
Stroup, D. F., Thacker, S. B., and Herndon, J. L., 1988, Application of multiple time series analysis to the estimation of pneumonia and influenza mortality by age 1962–83. Stat. Med., 7, 1045–1059.
Riley, T. V., O’Neill, G. L., Bowman, R. A., and Golledge, C. L., 1994, Clostridium difficile-associated diarrhoea: Epidemiological data from Western Australia. Epidemiol. Infect., 113, 13–20.
Thomas C., 2003, The epidemiology and control of Clostridium difficile infection in a Western Australian Hospital. PhD thesis, The University of Western Australia.
Thomas, C., Stevenson, M., and Riley, T. V., 2002, Clostridium difficile-associated diarrhoea: Epidemiological data from Western Australia following a modified antibiotic policy. Clin. Infect. Dis., 35, 1457–1462.
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López-Lozano, JM. et al. (2005). Applications of Time-series Analysis to Antibiotic Resistance and Consumption Data. In: Gould, I.M., van der Meer, J.W.M. (eds) Antibiotic Policies. Springer, Boston, MA. https://doi.org/10.1007/0-387-22852-7_24
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