Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis
Anthropological and socioeconomic factors contributing to global antimicrobial resistance: a univariate and multivariable analysis
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DOI：
10.1016/s2542-5196(18)30186-4
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发表时间：
2018-09-01
影响因子：
25.7
通讯作者：
Laxminarayan, Ramanan
中科院分区：
文献类型：
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作者：
Collignon, Peter;Beggs, John J.;Laxminarayan, Ramanan
对驱动全球抗菌素耐药性因素的了解是有限的。我们分析了世界范围内的抗菌素耐药性和抗生素消费量，以及许多潜在的影响因素。方法利用三个数据来源（耐药性地图、世卫组织2014年抗微生物药物耐药性报告和当代出版物），利用2008年至2014年的数据，我们创建了103个国家的两个全球抗微生物药物耐药性指数：大肠杆菌耐药性——对第三代头孢菌素和氟喹诺酮类药物耐药的大肠杆菌的全球平均患病率，以及总耐药性——对第三代头孢菌素、氟喹诺酮类药物和碳青霉烯类药物耐药的大肠杆菌和克雷伯氏菌的综合平均患病率，以及对甲氧西林耐药的金黄色葡萄球菌。抗生素消费数据来自IQVIA MIDAS数据库。使用世界银行数据库获取有关治理、教育、人均国内生产总值、保健支出和社区基础设施（如卫生设施）的数据。腐败指数是根据透明国际的数据得出的。我们使用单变量分析的简单相关性和多变量分析的逻辑回归模型来研究抗菌素耐药性与潜在影响因素之间的关系。在单变量分析中，人均GDP、教育、基础设施、公共卫生支出和抗生素消费均与两项抗菌素耐药性指标呈负相关，而较高的温度、较差的治理和私人卫生支出与公共卫生支出的比例呈正相关。在多变量回归分析（仅限于73个有抗生素消费数据的国家）中，考虑到指数变化对大肠杆菌耐药性（R-2 0.54）和总耐药性（R-2 0.75）的影响，更好的基础设施（p=0.014和p=0.0052）和更好的治理(p=0.025和p
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