The online version of this article (https://doi.org/10.1186/s12916-018-1121-8) contains supplementary material, which is available to authorized users.
Antibiotic-resistant bacteria (ARB) are selected by the use of antibiotics. The rational design of interventions to reduce levels of antibiotic resistance requires a greater understanding of how and where ARB are acquired. Our aim was to determine whether acquisition of ARB occurs more often in the community or hospital setting.
We used a mathematical model of the natural history of ARB to estimate how many ARB were acquired in each of these two environments, as well as to determine key parameters for further investigation. To do this, we explored a range of realistic parameter combinations and considered a case study of parameters for an important subset of resistant strains in England.
If we consider all people with ARB in the total population (community and hospital), the majority, under most clinically derived parameter combinations, acquired their resistance in the community, despite higher levels of antibiotic use and transmission of ARB in the hospital. However, if we focus on just the hospital population, under most parameter combinations a greater proportion of this population acquired ARB in the hospital.
It is likely that the majority of ARB are being acquired in the community, suggesting that efforts to reduce overall ARB carriage should focus on reducing antibiotic usage and transmission in the community setting. However, our framework highlights the need for better pathogen-specific data on antibiotic exposure, ARB clearance and transmission parameters, as well as the link between carriage of ARB and health impact. This is important to determine whether interventions should target total ARB carriage or hospital-acquired ARB carriage, as the latter often dominated in hospital populations.
Additional file 1: Additional information: Model equations and details of parameterisation. Additional results: Proportion of the population in hospital, variation in infection and mortality rates, histogram of parameter sets, sensitivity analyses. (PDF 177 kb)12916_2018_1121_MOESM1_ESM.pdf
WHO: Antimicrobial resistance: global report on surveillance. 2014.
Global Antimicrobial Resistance Surveillance System (GLASS) report: early implementation 2016–17. Geneva: World Health Organization; 2017. http://apps.who.int/iris/bitstream/10665/259744/1/9789241513449-eng.pdf?ua=1. Accessed July 2018.
Department of Health UK. Antimicrobial resistance (AMR) systems map. 2014. https://www.gov.uk/government/publications/antimicrobial-resistance-amr-systems-map. Accessed July 2018.
UK one health report: joint report on human and animal antibiotic use, sales and resistance. 2015. https://www.gov.uk/government/publications/uk-one-health-report-antibiotics-use-in-humans-and-animals. Accessed July 2018.
Review on Antimicrobial Resistance. Tackling drug-resistant infections globally: An overview of our work. https://amr-review.org/Publications.html. Accessed July 2018.
Austin DJ, Anderson RM. Studies of antibiotic resistance within the patient, hospitals and the community using simple mathematical models. Philos Trans R Soc Lond Ser B Biol Sci. 1999;354(1384):721–38. CrossRef
Moore LS, Freeman R, Gilchrist MJ, Gharbi M, Brannigan ET, Donaldson H, Livermore DM, Holmes AH. Homogeneity of antimicrobial policy, yet heterogeneity of antimicrobial resistance: antimicrobial non-susceptibility among 108,717 clinical isolates from primary, secondary and tertiary care patients in London. J Antimicrob Chemother. 2014;69(12):3409–22. CrossRefPubMedPubMedCentral
English Surveillance Programme for Antimicrobial Utilisation and Resistance (ESPAUR) 2010 to 2014: report 2015. 2015. https://www.gov.uk/government/publications/english-surveillance-programme-antimicrobial-utilisation-and-resistance-espaur-report. Accessed July 2018.
Health matters: preventing infections and reducing antimicrobial resistance. 2017. https://www.gov.uk/government/publications/health-matters-preventing-infections-and-reducing-amr/health-matters-preventing-infections-and-reducing-antimicrobial-resistance. Accessed July 2018.
Changes in the older resident care home population between 2001 and 2011. London: Office for National Statistics; 2014. http://webarchive.nationalarchives.gov.uk/20160105160709/http://www.ons.gov.uk/ons/dcp171776_373040.pdf. Accessed July 2018.
Iman RL, Helton JC, Campbell JE. An approach to sensitivity analysis of computer models. Part 2. Ranking of input variables, response-surface validation, distribution effect and technique synopsis. J Qual Technol. 1981;13(4):232–40. CrossRef
Review on Antimicrobial Resistance. Tackling drug-resistant infections globally. Final report and recommendations. 2016. http://amr-review.org/. Accessed July 2018.
Average number of available and occupied beds open overnight by sector. https://www.england.nhs.uk/statistics/statistical-work-areas/bedavailability-and-occupancy/bed-data-overnight/. Accessed July 2018.
Hospital Episode Statistics: Admitted Patient Care, England - 2013-2014. http://www.hscic.gov.uk/catalogue/PUB16719. Accessed July 2018.
World Development Indicators. http://data.worldbank.org/country/united-kingdom. Accessed July 2018.
English National Point Prevalence Survey on Healthcare-associated Infections and Antimicrobial Use, 2011. London: Health Protection Agency; 2012.
Public Health England. Annual epidemiological commentary: mandatory MRSA, MSSA and E. coli bacteraemia and C. difficile infection data, 2013/14. https://www.gov.uk/government/statistics/mrsa-mssa-and-e-coli-bacteraemia-and-c-difficile-infection-annual-epidemiological-commentary. Accessed July 2018.
- Quantifying where human acquisition of antibiotic resistance occurs: a mathematical modelling study
Gwenan M. Knight
Sarah R. Deeny
Luke S. P. Moore
Alan P. Johnson
Julie V. Robotham
Alison H. Holmes
- BioMed Central
Neu im Fachgebiet Allgemeinmedizin
Meistgelesene Bücher aus dem Fachgebiet
Mail Icon II