Filling the gaps in the global prevalence map of clinical antimicrobial resistance.
Filling the gaps in the global prevalence map of clinical antimicrobial resistance.
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DOI：
10.1073/pnas.2013515118
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发表时间：
2021-01-05
影响因子：
11.1
通讯作者：
Cappuccio A
中科院分区：
文献类型：
--
作者：
Oldenkamp R;Schultsz C;Mancini E;Cappuccio A
虽然抗生素耐药性是一个紧迫的全球性问题，但在低收入和中等收入国家（LMIC）存在着巨大的临床监测差距。我们填补了9种病原体的全球流行地图中的空白，这些病原体对19种（类）抗生素（代表75种独特的组合）具有耐药性，这是基于各国社会经济状况和广泛的监测数据之间的强相关性。我们对碳青霉烯类耐药鲍曼不动杆菌和第三代头孢菌素耐药大肠埃希菌的估计使目前诊断能力不足的国家的22亿多人受益。我们展示了如何根据估计的流行程度（中东国家），1998年至2017年的相对流行率增加（撒哈拉以南非洲国家）以及新监测数据（太平洋岛屿）可实现的模型性能的改善来优先考虑结构性监测投资。监测对于遏制全球范围内不断增加的抗菌素耐药性（AMR）至关重要。迫切需要负担得起的方法来优先考虑AMR监测工作，特别是在资源有限的低收入和中等收入国家（LMIC）。虽然社会经济特征与临床AMR患病率相关，但这种相关性尚未用于估计缺乏监测的国家的AMR患病率。我们在一套β-二项式主成分回归模型中捕获了对19种（类）抗生素耐药的9种病原体的AMR患病率与社会经济特征之间的统计关系。ResistanceMap的患病率数据与根据5，595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患病率和时间趋势。我们的方法提供了LMIC中大多数优先病原体的临床AMR患病率的稳健估计（9种病原体中有6种的交叉验证q2 > 0.78）。通过补充监测数据，全球87%的国家（占全球人口的99%）现已获得信息。根据优先病原体的不同，我们的估计将使生活在目前诊断能力不足的国家的21亿至49亿人受益。通过估计全球AMR患病率，我们的方法允许数据驱动的监测工作的优先级。对于碳青霉烯类耐药鲍曼不动杆菌和第三代头孢菌素耐药大肠埃希菌，根据估计值的大小，关注的特定国家位于中东;根据1998年至2017年的相对流行率增加，关注的特定国家位于撒哈拉以南非洲;根据总体模型覆盖率和性能的改善，关注的特定国家位于太平洋岛屿。
While antimicrobial resistance is an urgent global problem, substantial clinical surveillance gaps exist in low- and middle-income countries (LMICs). We fill the gaps in the global prevalence map of nine pathogens, resistant to 19 (classes of) antibiotics (representing 75 unique combinations), based on the robust correlation between countries’ socioeconomic profiles and extensive surveillance data. Our estimates for carbapenem-resistant Acinetobacter baumannii and third-generation cephalosporin-resistant Escherichia coli benefit over 2.2 billion people in countries with currently insufficient diagnostic capacity. We show how structural surveillance investments can be prioritized based on the magnitude of prevalence estimated (Middle Eastern countries), the relative prevalence increase over 1998 to 2017 (sub-Saharan African countries), and the improvement of model performance achievable with new surveillance data (Pacific Islands). 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 q2 > 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 Acinetobacter baumannii and third-generation cephalosporin-resistant Escherichia coli, 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.
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影响因子：
3.7
作者：
Collignon P;Athukorala PC;Senanayake S;Khan F
通讯作者：
Khan F
影响因子：
8.4
作者：
Alvarez-Uria, Gerardo;Gandra, Sumanth;Laxminarayan, Ramanan
通讯作者：
Laxminarayan, Ramanan
影响因子：
5
作者：
Ginting, Franciscus;Sugianli, Adhi Kristianto;van Leth, Frank
通讯作者：
van Leth, Frank
影响因子：
34.3
作者：
Allwell-Brown, Gbemisola;Hussain-Alkhateeb, Laith;Johansson, Emily White
通讯作者：
Johansson, Emily White
影响因子：
5.2
作者：
Blommaert, A.;Marais, C.;Beutels, P.
通讯作者：
Beutels, P.