Oldenkamp Rik, Schultsz Constance, Mancini Emiliano, Cappuccio Antonio
Amsterdam Institute for Global Health and Development, 1105 BP Amsterdam, The Netherlands;
Department of Global Health, Amsterdam University Medical Centres, Location AMC, 1105 AZ Amsterdam, The Netherlands.
Proc Natl Acad Sci U S A. 2021 Jan 5;118(1). doi: 10.1073/pnas.2013515118.
Surveillance is critical in containing globally increasing antimicrobial resistance (AMR). Affordable methodologies to prioritize AMR surveillance efforts are urgently needed, especially in low- and middle-income countries (LMICs), where resources are limited. While socioeconomic characteristics correlate with clinical AMR prevalence, this correlation has not yet been used to estimate AMR prevalence in countries lacking surveillance. We captured the statistical relationship between AMR prevalence and socioeconomic characteristics in a suite of beta-binomial principal component regression models for nine pathogens resistant to 19 (classes of) antibiotics. Prevalence data from ResistanceMap were combined with socioeconomic profiles constructed from 5,595 World Bank indicators. Cross-validated models were used to estimate clinical AMR prevalence and temporal trends for countries lacking data. Our approach provides robust estimates of clinical AMR prevalence in LMICs for most priority pathogens (cross-validated > 0.78 for six out of nine pathogens). By supplementing surveillance data, 87% of all countries worldwide, which represent 99% of the global population, are now informed. Depending on priority pathogen, our estimates benefit 2.1 to 4.9 billion people living in countries with currently insufficient diagnostic capacity. By estimating AMR prevalence worldwide, our approach allows for a data-driven prioritization of surveillance efforts. For carbapenem-resistant and third-generation cephalosporin-resistant , specific countries of interest are located in the Middle East, based on the magnitude of estimates; sub-Saharan Africa, based on the relative prevalence increase over 1998 to 2017; and the Pacific Islands, based on improving overall model coverage and performance.
监测对于遏制全球日益增长的抗菌药物耐药性（AMR）至关重要。迫切需要经济实惠的方法来确定AMR监测工作的优先级，特别是在资源有限的低收入和中等收入国家（LMICs）。虽然社会经济特征与临床AMR流行率相关，但这种相关性尚未用于估计缺乏监测的国家的AMR流行率。我们在一组β-二项式主成分回归模型中捕捉了19种（类）抗生素耐药的9种病原体的AMR流行率与社会经济特征之间的统计关系。来自ResistanceMap的流行率数据与根据5595个世界银行指标构建的社会经济概况相结合。交叉验证模型用于估计缺乏数据的国家的临床AMR流行率和时间趋势。我们的方法为大多数优先病原体提供了对LMICs临床AMR流行率的可靠估计（9种病原体中有6种交叉验证>0.78）。通过补充监测数据，现在全球87%的国家（代表全球99%的人口）已了解情况。根据优先病原体的不同，我们的估计惠及了生活在目前诊断能力不足国家的21亿至49亿人。通过估计全球AMR流行率，我们的方法允许对监测工作进行数据驱动的优先级排序。对于耐碳青霉烯类和耐第三代头孢菌素类，根据估计的规模，中东地区有特定的感兴趣国家；根据1998年至2017年相对流行率的增加，撒哈拉以南非洲地区有特定的感兴趣国家；根据整体模型覆盖范围和性能的改善，太平洋岛屿地区有特定的感兴趣国家。
