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Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis

Collignon, Peter; Beggs, John J; Walsh, Timothy R; Gandra, Sumanth; Laxminarayan, Ramanan

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Background Understanding of the factors driving global antimicrobial resistance is limited. We analysed antimicrobial resistance and antibiotic consumption worldwide versus many potential contributing factors. Methods Using three sources of data (ResistanceMap, the WHO 2014 report on antimicrobial resistance, and contemporary publications), we created two global indices of antimicrobial resistance for 103 countries using data from 2008 to 2014: Escherichia coli resistance—the global...[Show more]

dc.contributor.authorCollignon, Peter
dc.contributor.authorBeggs, John J
dc.contributor.authorWalsh, Timothy R
dc.contributor.authorGandra, Sumanth
dc.contributor.authorLaxminarayan, Ramanan
dc.date.accessioned2022-11-23T02:46:13Z
dc.date.available2022-11-23T02:46:13Z
dc.identifier.issn2542-5196
dc.identifier.urihttp://hdl.handle.net/1885/280406
dc.description.abstractBackground Understanding of the factors driving global antimicrobial resistance is limited. We analysed antimicrobial resistance and antibiotic consumption worldwide versus many potential contributing factors. Methods Using three sources of data (ResistanceMap, the WHO 2014 report on antimicrobial resistance, and contemporary publications), we created two global indices of antimicrobial resistance for 103 countries using data from 2008 to 2014: Escherichia coli resistance—the global average prevalence of E coli bacteria that were resistant to third-generation cephalosporins and fluoroquinolones, and aggregate resistance—the combined average prevalence of E coli and Klebsiella spp resistant to third-generation cephalosporins, fluoroquinolones, and carbapenems, and meticillin-resistant Staphylococcus aureus. Antibiotic consumption data were obtained from the IQVIA MIDAS database. The World Bank DataBank was used to obtain data for governance, education, gross domestic product (GDP) per capita, health-care spending, and community infrastructure (eg, sanitation). A corruption index was derived using data from Transparency International. We examined associations between antimicrobial resistance and potential contributing factors using simple correlation for a univariate analysis and a logistic regression model for a multivariable analysis. Findings In the univariate analysis, GDP per capita, education, infrastructure, public health-care spending, and antibiotic consumption were all inversely correlated with the two antimicrobial resistance indices, whereas higher temperatures, poorer governance, and the ratio of private to public health expenditure were positively correlated. In the multivariable regression analysis (confined to the 73 countries for which antibiotic consumption data were available) considering the effect of changes in indices on E coli resistance (R2 0·54) and aggregate resistance (R2 0·75), better infrastructure (p=0·014 and p=0·0052) and better governance (p=0·025 and p<0·0001) were associated with lower antimicrobial resistance indices. Antibiotic consumption was not significantly associated with either antimicrobial resistance index in the multivariable analysis (p=0·64 and p=0·070). Interpretation Reduction of antibiotic consumption will not be sufficient to control antimicrobial resistance because contagion—the spread of resistant strains and resistance genes—seems to be the dominant contributing factor. Improving sanitation, increasing access to clean water, and ensuring good governance, as well as increasing public health-care expenditure and better regulating the private health sector are all necessary to reduce global antimicrobial resistance. Funding None.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherElsevier B.V
dc.rights© 2018 The authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceThe Lancet Planetary Health
dc.titleAnthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume2
dc.date.issued2018
local.identifier.absfor000000 - Internal ANU use only
local.identifier.ariespublicationu3102795xPUB1369
local.publisher.urlhttps://www.sciencedirect.com/
local.type.statusPublished Version
local.contributor.affiliationCollignon, Peter, College of Health and Medicine, ANU
local.contributor.affiliationBeggs, John J, Monarch Institute
local.contributor.affiliationWalsh, Timothy R, Cardiff University
local.contributor.affiliationGandra, Sumanth, Center for Disease Dynamics, Economics & Policy
local.contributor.affiliationLaxminarayan, Ramanan, Center for Disease Dynamics, Economics & Policy
local.bibliographicCitation.issue9
local.bibliographicCitation.startpagee398
local.bibliographicCitation.lastpagee405
local.identifier.doi10.1016/S2542-5196(18)30186-4
local.identifier.absseo200104 - Prevention of human diseases and conditions
dc.date.updated2021-11-28T07:29:37Z
local.identifier.scopusID2-s2.0-85052503709
dcterms.accessRightsOpen Access
dc.provenanceThis is an Open Access article under the CC BY-NC-ND 4.0 license.
dc.rights.licenseCreative Commons Attribution licence
CollectionsANU Research Publications

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