Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Objectives: To assess the association between country income status and national prevalence of invasive infections caused by the top-ranked bacteria on the WHO priority list: carbapenem-resistant (CR) Acinetobacter spp., Klebsiella spp. and Pseudomonas aeruginosa; third-generation cephalosporin-resistant (3GCR) Escherichia coli and Klebsiella spp.; and MRSA and vancomycin-resistant Enterococcus faecium (VR E. faecium).
Methods: Active surveillance systems providing yearly prevalence data from 2012 onwards for the selected bacteria were included. The gross national income (GNI) per capita was used as the indicator for income status of each country and was log transformed to account for non-linearity. The association between antibiotic prevalence data and GNI per capita was investigated individually for each bacterium through linear regression.
Results: Surveillance data were available from 67 countries: 38 (57%) were high income, 16 (24%) upper-middle income, 11 (16%) lower-middle income and two (3%) low income countries. The regression showed significant inverse association (P<0.0001) between resistance prevalence of invasive infections and GNI per capita. The highest rate of increase per unit decrease in log GNI per capita was observed in 3GCR Klebsiella spp. (22.5%, 95% CI 18.2%-26.7%), CR Acinetobacter spp. (19.2% 95% CI 11.3%-27.1%) and 3GCR E. coli (15.3%, 95% CI 11.6%-19.1%). The rate of increase per unit decrease in log GNI per capita was lower in MRSA (9.5%, 95% CI 5.2%-13.7%).
Conclusions: The prevalence of invasive infections caused by the WHO top-ranked antibiotic-resistant bacteria is inversely associated with GNI per capita at the global level. Public health interventions designed to limit the burden of antimicrobial resistance should also consider determinants of poverty and inequality, especially in lower-middle income and low income countries.
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Source |
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http://dx.doi.org/10.1093/jac/dkz381 | DOI Listing |
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