Antimicrobial Research Laboratory, Department of Bacterial and Inflammatory Diseases, National Public Health Institute, Finland
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Abstract |
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Keywords: resistance , surveillance , antibiotic consumption , mathematical models
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Introduction |
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How might we control antimicrobial resistance? Reducing and targeting antimicrobial use is important, but how should this be done wisely? Physicians need antimicrobial agents in their daily life. These drugs are the main curative class of drugs in physicians' treatment arsenal, and physicians' success is very much dependent on their use. Recommendations to change prescription behaviour need a good scientific basis. Therefore, we should develop tools to predict development of resistance when different antibiotic policies are used.
Mathematical models are important tools that can be used to understand aspects of various processes and to make predictions.38 During recent decades, huge amounts of resistance surveillance data have been produced. In contrast, over the same period, there has been an almost total lack of antimicrobial consumption data. When we began to study the relationship between bacterial resistance and antimicrobial consumption more than two decades ago, hospital data were easy to obtain.9 However, it was almost impossible to get figures on antimicrobial consumption from outpatient practice.10 This information was in the hands of the pharmaceutical industry and, with a few exceptions, they were not interested in sharing data on antibiotic sales. In Finland, the situation changed in the 1990s when the authorities responsible for medicines began releasing all consumption data, even at the community level, for research purposes.11 In Europe, a cornerstone of progress was negotiation, during which researchers succeeded in buying consumption data from a company responsible for antibiotic consumption data collection.12 Today, the network for European Surveillance of Antibiotic Consumption is collecting data on antibiotic sales Europe-wide.13
Now there is a real opportunity, having both surveillance data of bacterial resistance and consumption of antimicrobial agents, to probe different kinds of mathematical models to try to predict the future of bacterial resistance.
The paper by Magee14 in this issue contains delightful philosophical considerations, although the theoretical basis of this model may not be complete. The approach is different from those models published previously.38 In planning for the future with the assistance of mathematical models, several aspects need to be considered. It is of primary importance to understand that mathematical modelling is a purely theoretical exercise and the results obtained from a model are only as good as the stated assumptions made.
Therefore the first comment on Magee's model concerns a lack of accurate data on antimicrobial consumption. I miss most a definition of the bacterial species that this theory concerns. Readers want to see how the model applies to the real world. It is reasonable to assume that different bacteria will behave differently. The epidemiology of Streptococcus pneumoniae is different from that of Escherichia coli or Staphylococcus aureus. Most of us have E. coli in our gut, but only a fraction of us are colonized with the pneumococcus or S. aureus. Second, most of the bacteria are epidemic. Even E. coli causing urinary tract infections have been said to be transmissible15 or of animal origin.16 Thus, why would only human antibiotic use have an impact on the development of resistance?17 Methicillin-resistant S. aureus is also very different. Multiresistant clones are spreading despite the level of antibiotic usage. Hygiene measures also have an impact on the spread of bacteria, at least in hospitals,1 but why not also in the community, especially when most of the bacteria are epidemic?
I have always wondered, why do 8090% of S. aureus isolates produce penicillinase uniformly all over the world? It has been suggested that this is caused by nasal lysozyme production that favours penicillinase-producing staphylococci.18 If this is the case, antibiotic consumption may have had only a minor role in the spread of penicillinase-producing staphylococci. Third, we know that resistance factors in E. coli are often in gene cassettes containing resistance genes to several antibiotic classes at the same time.19 So, reduction of one antibiotic class may not necessarily have any direct influence on the existence of these gene cassettes and the resistance levels may remain unchanged. For instance, it was reasonable to expect that decreased use of sulphonamides would not actually result in decreased resistance to sulphonamides in the UK.20
The model of Magee predicts a continuous increase of resistance. There are, however, many examples of stable resistance levels despite heavy antibiotic use. Although an exception, Streptococcus pyogenes is still completely susceptible to penicillin. In addition, how can this model accunt for studies that show decreases of resistance of pneumococci in Iceland21 and group A streptococcus in Finland?22 In addition, human population density may have a role in the spread of resistant bacteria. Transfer of bacteria is different in urban and remote geographical areas.8 Seasonal variation in antibiotic use is a common phenomenon, but not for all antimicrobials and not in all communities.13 Furthermore, adaptation differences of bacteria may cause problems for mathematical models.23,24
Development of bacterial resistance is a complex issue. Thus we need to develop different kinds of models to understand better the spread of bacteria resistant to antimicrobial agents. The article written by Magee is an example of one model. It is now open for discussion and further development. It is to be hoped that development and refinement of current models will one day yield useful tools to predict how different bacteria will behave under different selection pressures caused by antimicrobial agents and many other factors.
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Editorial note |
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Background of this article |
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References |
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2. Talbot TR, Poehling KA, Hartert TV et al. Reduction in high rates of antibiotic-nonsusceptible invasive pneumococcal disease in Tennessee after introduction of the pneumococcal conjugate vaccine. Clin Infect Dis 2004; 39: 6418.[CrossRef][ISI][Medline]
3. Austin DJ, Kakehashi M, Anderson RM. The transmission dynamics of antibiotic-resistant bacteria: the relationship between resistance in commensal organisms and antibiotic consumption. Proc Biol Sci 1997; 264: 162938.[CrossRef][ISI][Medline]
4.
Austin DJ, Kristinsson KG, Anderson RM. The relationship between the volume of antimicrobial consumption in human communities and the frequency of resistance. Proc Natl Acad Sci USA 1999; 96: 11526.
5.
Lipsitch M, Bergstrom CT, Levin BR. The epidemiology of antibiotic resistance in hospitals; paradoxes and prescriptions. Proc Natl Acad Sci USA 2000; 97: 193843.
6. Bonten MJ, Austin DJ, Lipsitch M. Understanding the spread of antibiotic resistant pathogens in hospitals: mathematical models as tools for control. Clin Infect Dis 2001; 33: 173946.[CrossRef][ISI][Medline]
7.
Bergstrom CT, Lo M, Lipsitch M. Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. Proc Natl Acad Sci USA 2004; 101: 1328590.
8.
Smith DL, Levin SA, Laxminarayan R. Strategic interactions in multi-institutional epidemics of antibiotic resistance. Proc Natl Acad Sci USA 2005; 102: 31538.
9. Huovinen P, Mäntyjärvi R, Toivanen P. Trimethoprim resistance in hospitals. Br Med J 1982; 284: 7824.[ISI][Medline]
10. Huovinen P, Renkonen O-V, Pulkkinen L et al. Trimethoprim resistance of Escherichia coli in outpatients in Finland after ten years' use of plain trimethoprim. J Antimicrob Chemother 1985; 16: 43541.[Abstract]
11. Seppälä H, Klaukka T, Lehtonen R et al. Outpatient erythromycin uselink to increased erythromycin resistance in group A streptococci. Clin Infect Dis 1995; 21: 137885.[ISI][Medline]
12. Cars O, Molstad S, Melander A. Variation in antibiotic use in the European Union. Lancet 2001; 357: 18513.[CrossRef][ISI][Medline]
13. Goossens H, Ferech M, Vander Stichele R et al. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet 2005; 365: 57987.[CrossRef][ISI][Medline]
14. Magee JD. The resistance ratchet: theoretical implications of cyclic selection pressure. J Antimicrob Chemother 2005; 56: doi:10.1093/jac/dki229
15.
Manges AR, Johnson JR, Foxman B et al. Widespread distribution of urinary tract infections caused by a multidrug-resistant Escherichia coli clonal group. N Engl J Med 2001; 345: 100713.
16. Ramchandani M, Manges AR, DebRoy C et al. Possible animal origin of human-associated, multidrug-resistant, uropathogenic Escherichia coli. Clin Infect Dis 2005; 40: 2517.[CrossRef][ISI][Medline]
17.
Smith DL, Harris AD, Johnson JA et al. Animal antibiotic use has an early but important impact on the emergence of antibiotic resistance in human commensal bacteria. Proc Natl Acad Sci USA 2002; 99: 64349.
18. Millar M, Lacey RW. Possible selection of beta-lactamase-producing Staphylococcus aureus by lysozyme. Lancet 1984; 2: 987.
19.
Huovinen P, Sundström L, Swedberg G et al. Minireview. Trimethoprim and sulfonamide resistance. Antimicrob Agents Chemother 1995; 39: 27989.
20. Enne VI, Livermore DM, Stephens P et al. Persistence of sulphonamide resistance in Escherichia coli in the UK despite national prescribing restriction. Lancet 2001; 357: 13258.[CrossRef][ISI][Medline]
21.
Arason VA, Kristinsson KG, Sigurdsson JA et al. Do antimicrobials increase the carriage rate of penicillin resistant pneumococci in children? Cross sectional prevalence study. BMJ 1996; 313: 38791.
22.
Seppälä H, Klaukka T, Vuopio-Varkila J et al. The effects of changes in the consumption of macrolide antibiotics on erythromycin resistance in group A streptococci in Finland. N Engl J Med 1997; 337: 4416.
23. Andersson DI. Persistence of antibiotic resistant bacteria. Curr Opin Microbiol 2003; 6: 4526.[CrossRef][ISI][Medline]
24.
Gustafsson I, Cars O, Andersson DI. Fitness of antibiotic resistant Staphylococcus epidermidis assessed by competition on the skin of human volunteers. J Antimicrob Chemother 2003; 52: 25863.