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Abstract

Background

The spread of antimicrobial resistance (AMR) is of worldwide concern. Public health policymakers and pharmaceutical companies pursuing antibiotic development require accurate predictions about the future spread of AMR.

Aim

We aimed to identify and model temporal and geographical patterns of AMR spread and to predict future trends based on a slow, intermediate or rapid rise in resistance.

Methods

We obtained data from five antibiotic resistance surveillance projects spanning the years 1997 to 2015. We aggregated the isolate-level or country-level data by country and year to produce country–bacterium–antibiotic class triads. We fitted both linear and sigmoid models to these triads and chose the one with the better fit. For triads that conformed to a sigmoid model, we classified AMR progression into one of three characterising paces: slow, intermediate or fast, based on the sigmoid slope. Within each pace category, average sigmoid models were calculated and validated.

Results

We constructed a database with 51,670 country–year–bacterium–antibiotic observations, grouped into 7,440 country–bacterium–antibiotic triads. A total of 1,037 triads (14%) met the inclusion criteria. Of these, 326 (31.4%) followed a sigmoid (logistic) pattern over time. Among 107 triads for which both sigmoid and linear models could be fit, the sigmoid model was a better fit in 84%. The sigmoid model deviated from observed data by a median of 6.5%; the degree of deviation was related to the pace of spread.

Conclusion

We present a novel method of describing and predicting the spread of antibiotic-resistant organisms.

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This work is licensed under a Creative Commons Attribution 4.0 International License.
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/content/10.2807/1560-7917.ES.2020.25.23.1900387
2020-06-11
2024-11-16
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2020.25.23.1900387
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References

  1. World Health Organization (WHO). Global action plan on antimicrobial resistance. Geneva: WHO; 2015. Available from: http://apps.who.int/iris/bitstream/handle/10665/193736/9789241509763_eng.pdf?sequence=1
  2. The White House. Executive order - combating antibiotic-resistant bacteria. Washington, DC, The White House; 2014. Available from: https://www.cdc.gov/drugresistance/pdf/executive-order_ar.pdf
  3. United Nations (UN). Draft political declaration of the high-level meeting of the General Assembly on antimicrobial resistance. New York: UN; 2016. Available from: https://www.un.org/pga/71/wp-content/uploads/sites/40/2016/09/DGACM_GAEAD_ESCAB-AMR-Draft-Political-Declaration-1616108E.pdf
  4. World Health Organization (WHO). Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. Geneva: WHO; 2017. Available from: http://www.who.int/medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_NM_WHO.pdf?ua=1
  5. Centers for Disease Control and Prevention (CDC). Antibiotic resistance threats in the United States 2019. Atlanta: CDC; 2019. Available from: https://www.cdc.gov/drugresistance/biggest_threats.html
  6. Cosgrove SE, Carmeli Y. The impact of antimicrobial resistance on health and economic outcomes. Clin Infect Dis. 2003;36(11):1433-7.  https://doi.org/10.1086/375081  PMID: 12766839 
  7. Naylor NR, Atun R, Zhu N, Kulasabanathan K, Silva S, Chatterjee A, et al. Estimating the burden of antimicrobial resistance: a systematic literature review. Antimicrob Resist Infect Control. 2018;7(1):58.  https://doi.org/10.1186/s13756-018-0336-y  PMID: 29713465 
  8. Cosgrove SE. The relationship between antimicrobial resistance and patient outcomes: mortality, length of hospital stay, and health care costs. Clin Infect Dis. 2006;42(Suppl 2):S82-9.  https://doi.org/10.1086/499406  PMID: 16355321 
  9. Cosgrove SE, Kaye KS, Eliopoulous GM, Carmeli Y. Health and economic outcomes of the emergence of third-generation cephalosporin resistance in Enterobacter species. Arch Intern Med. 2002;162(2):185-90.  https://doi.org/10.1001/archinte.162.2.185  PMID: 11802752 
  10. Cosgrove SE, Qi Y, Kaye KS, Harbarth S, Karchmer AW, Carmeli Y. The impact of methicillin resistance in Staphylococcus aureus bacteremia on patient outcomes: mortality, length of stay, and hospital charges. Infect Control Hosp Epidemiol. 2005;26(2):166-74.  https://doi.org/10.1086/502522  PMID: 15756888 
  11. Schwaber MJ, Navon-Venezia S, Kaye KS, Ben-Ami R, Schwartz D, Carmeli Y. Clinical and economic impact of bacteremia with extended- spectrum-beta-lactamase-producing Enterobacteriaceae. Antimicrob Agents Chemother. 2006;50(4):1257-62.  https://doi.org/10.1128/AAC.50.4.1257-1262.2006  PMID: 16569837 
  12. Temkin E, Fallach N, Almagor J, Gladstone BP, Tacconelli E, Carmeli Y, et al. Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study. Lancet Glob Health. 2018;6(9):e969-79.  https://doi.org/10.1016/S2214-109X(18)30278-X  PMID: 30103998 
  13. Tacconelli E, Sifakis F, Harbarth S, Schrijver R, van Mourik M, Voss A, et al. Surveillance for control of antimicrobial resistance. Lancet Infect Dis. 2018;18(3):e99-106.  https://doi.org/10.1016/S1473-3099(17)30485-1  PMID: 29102325 
  14. Birkegård AC, Halasa T, Toft N, Folkesson A, Græsbøll K. Send more data: a systematic review of mathematical models of antimicrobial resistance. Antimicrob Resist Infect Control. 2018;7(1):117.  https://doi.org/10.1186/s13756-018-0406-1  PMID: 30288257 
  15. Alvarez-Uria G, Gandra S, Mandal S, Laxminarayan R. Global forecast of antimicrobial resistance in invasive isolates of Escherichia coli and Klebsiella pneumoniae. Int J Infect Dis. 2018;68:50-3.  https://doi.org/10.1016/j.ijid.2018.01.011  PMID: 29410253 
  16. O’Neill J; Review on Antimicrobial Resistance. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. London: HM Government; 2014. Available from: https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf
  17. 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(3):1152-6.  https://doi.org/10.1073/pnas.96.3.1152  PMID: 9927709 
  18. Knight GM, Costelloe C, Murray KA, Robotham JV, Atun R, Holmes AH. Addressing the unknowns of antimicrobial resistance: quantifying and mapping the drivers of burden. Clin Infect Dis. 2018;66(4):612-6.  https://doi.org/10.1093/cid/cix765  PMID: 29020246 
  19. Clinical and Laboratory Standards Institute (CLSI). Performance standards for antimicrobial susceptibility testing. 19th ed. Wayne, PA; 2009.
  20. European Antimicrobial Resistance Surveillance Network (EARS-Net). Surveillance atlas of infectious diseases – antimicrobial resistance. Stockholm: European Centre for Disease Prevention and Control. [Accessed: 22 May 2019]. Available from: http://atlas.ecdc.europa.eu/public/index.aspx
  21. Center for Disease Dynamics Economics and Policy (CDDEP). ResistanceMap. Silver Spring: CDDEP. [Accessed: 22 May 2019]. Available from: https://resistancemap.cddep.org/
  22. World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology. ATC Structure and principles. Oslo: WHO Collaborating Centre for Drug Statistics Methodology; 2018. [Accessed: 22 May 2019]. Available from: https://www.whocc.no/atc/structure_and_principles/
  23. Efron B. Regression and ANOVA with Zero-One data: measures of residual variation. J Am Stat Assoc. 1978;73(361):113-21.  https://doi.org/10.1080/01621459.1978.10480013 
  24. Munch-Petersen E, Boundy C. Yearly incidence of penicillin-resistant staphylococci in man since 1942. Bull World Health Organ. 1962;26:241-52. PMID: 14477181 
  25. Planet PJ. Life after uSA300: The rise and fall of a superbug. J Infect Dis. 2017;215(suppl_1):S71-7.
  26. Henriques-Normark B, Blomberg C, Dagerhamn J, Bättig P, Normark S. The rise and fall of bacterial clones: Streptococcus pneumoniae. Nat Rev Microbiol. 2008;6(11):827-37.  https://doi.org/10.1038/nrmicro2011  PMID: 18923410 
  27. Opatowski L, Guillemot D, Boëlle P-Y, Temime L. Contribution of mathematical modeling to the fight against bacterial antibiotic resistance. Curr Opin Infect Dis. 2011;24(3):279-87.  https://doi.org/10.1097/QCO.0b013e3283462362  PMID: 21467930 
  28. Seppälä H, Klaukka T, Vuopio-Varkila J, Muotiala A, Helenius H, Lager K, et al. The effect of changes in the consumption of macrolide antibiotics on erythromycin resistance in group A streptococci in Finland. N Engl J Med. 1997;337(7):441-6.  https://doi.org/10.1056/NEJM199708143370701  PMID: 9250845 
  29. Edgeworth JD. Has decolonization played a central role in the decline in UK methicillin-resistant Staphylococcus aureus transmission? A focus on evidence from intensive care. J Antimicrob Chemother. 2011;66(Suppl 2):ii41-7.  https://doi.org/10.1093/jac/dkq325  PMID: 20852273 
  30. Schwaber MJ, Carmeli Y. An ongoing national intervention to contain the spread of carbapenem-resistant enterobacteriaceae. Clin Infect Dis. 2014;58(5):697-703.  https://doi.org/10.1093/cid/cit795  PMID: 24304707 
  31. Heesterbeek H, Anderson RM, Andreasen V, Bansal S, De Angelis D, Dye C, et al. Modeling infectious disease dynamics in the complex landscape of global health. Science. 2015;347(6227):aaa4339.  https://doi.org/10.1126/science.aaa4339  PMID: 25766240 
Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Noga+Fallach&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Fallach Noga</a>, <a href="/search?value1=Yaakov+Dickstein&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Dickstein Yaakov</a>, <a href="/search?value1=Erez+Silberschein&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Silberschein Erez</a>, <a href="/search?value1=John+Turnidge&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Turnidge John</a>, <a href="/search?value1=Elizabeth+Temkin&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Temkin Elizabeth</a>, <a href="/search?value1=Jonatan+Almagor&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Almagor Jonatan</a>, <a href="/search?value1=Yehuda+Carmeli&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Carmeli Yehuda</a>, <a href="/search?value1=on+behalf+of+the+DRIVE-AB+Consortium&option1=author&noRedirect=true" class="nonDisambigAuthorLink">on behalf of the DRIVE-AB Consortium</a></span>. Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level. <a href="/content/ecdc">Euro Surveill.</a> 2020;25(23):pii=1900387. <a href="https://doi.org/10.2807/1560-7917.ES.2020.25.23.1900387" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2020.25.23.1900387</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 18 Jun 2019;&nbsp;&nbsp; <span class="generated">Accepted</span>: 07 Jan 2020 </span> </p>
/content/10.2807/1560-7917.ES.2020.25.23.1900387
/content/10.2807/1560-7917.ES.2020.25.23.1900387
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