Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis. Issue 9 (September 2018)

• Record Type: Journal Article
• Authors: Collignon, Peter; Beggs, John J; Walsh, Timothy R; Gandra, Sumanth; Laxminarayan, Ramanan
• Is Part Of: Lancet. Volume 2:Issue 9(2018)
• Page Start: e398
• Page End: e405
• Publication Date: 2018-09
• Subjects: Global environmental change -- Periodicals; Climatic changes -- Government policy -- Periodicals; Ecology -- Periodicals; Human ecology -- Periodicals; Public health administration -- Periodicals; 304.2
• DOI: 10.1016/S2542-5196(18)30186-4
• Languages: English
• ISSNs: 2542-5196

Abstract

Summary: 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 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 ( R 2 0·54) and aggregate resistance ( R 2 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.