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      National action plans for antimicrobial resistance and variations in surveillance data platforms Translated title: Plans d'action nationaux concernant la résistance aux antimicrobiens et variations au niveau des plateformes de données de surveillance Translated title: Planes de acción nacional orientados a la resistencia antimicrobiana, y variaciones en las plataformas de datos sobre vigilancia Translated title: خطط العمل الوطنية لمقاومة مضادات الميكروبات والاختلافات في منصات بيانات المراقبة Translated title: 国家抗微生物药物耐药性行动计划和监测数据平台的数据变化 Translated title: Национальные планы действий по борьбе с устойчивостью к противомикробным препаратам и различия в платформах данных эпиднадзора

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          Abstract

          Objective

          To assess how national antimicrobial susceptibility data used to inform national action plans vary across surveillance platforms.

          Methods

          We identified available open-access, supranational, interactive surveillance platforms and cross-checked their data in accordance with the World Health Organization’s (WHO’s) Data Quality Assurance: module 1. We compared platform usability and completeness of time-matched data on the antimicrobial susceptibilities of four blood isolate species: Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus and Streptococcus pneumoniae from WHO’s Global Antimicrobial Resistance and Use Surveillance System, European Centre for Disease Control’s (ECDC’s) network and Pfizer’s Antimicrobial Testing Leadership and Surveillance database. Using Bland–Altman analysis, paired t-tests, and Wilcoxon signed-rank tests, we assessed susceptibility data and number of isolate concordances between platforms.

          Findings

          Of 71 countries actively submitting data to WHO, 28 also submit to Pfizer’s database; 19 to ECDC; and 16 to all three platforms. Limits of agreement between WHO’s and Pfizer’s platforms for organism–country susceptibility data ranged from −26% to 35%. While mean susceptibilities of WHO’s and ECDC‘s platforms did not differ (bias: 0%, 95% confidence interval: −2 to 2), concordance between organism–country susceptibility was low (limits of agreement −18% to 18%). Significant differences exist in isolate numbers reported between WHO–Pfizer (mean of difference: 674, P-value: < 0.001, and WHO–ECDC (mean of difference: 192, P-value: 0.04) platforms.

          Conclusion

          The considerable heterogeneity of nationally submitted data to commonly used antimicrobial resistance surveillance platforms compromises their validity, thus undermining local and global antimicrobial resistance strategies. Hence, we need to understand and address surveillance platform variability and its underlying mechanisms.

          Résumé

          Objectif

          Évaluer de quelle manière les données nationales relatives à la sensibilité aux antimicrobiens, qui servent à établir des plans d'action nationaux, varient d'une plateforme de surveillance à l'autre.

          Méthodes

          Nous avons identifié les plateformes de surveillance interactives, supranationales et en libre accès, et avons recoupé leurs données conformément au module 1 du Contrôle de la qualité des données de l'Organisation mondiale de la Santé (OMS). Nous avons comparé la facilité d'utilisation et l'exhaustivité des informations synchronisées concernant la sensibilité aux antimicrobiens pour quatre types d'isolats d'hémoculture: Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus et Streptococcus pneumoniae, issus du Système mondial de surveillance de la résistance aux antimicrobiens de l'OMS, du réseau du Centre européen de prévention et de contrôle des maladies (ECDC) ainsi que de la base de données du Programme de surveillance de la résistance aux antimicrobiens (ATLAS) de Pfizer. À l'aide d'une analyse de Bland-Altman, de tests t jumelés et de tests des rangs signés de Wilcoxon, nous avons évalué les données relatives à la sensibilité et le nombre d'isolats concordants entre les plateformes.

          Résultats

          Sur 71 pays qui transmettent activement leurs données à l'OMS, 28 les fournissent également à la base de données de Pfizer; 19 à l'ECDC; et 16 à l'ensemble des trois plateformes. Les limites de l'accord entre les plateformes de l'OMS et de Pfizer pour les données de sensibilité par organisme–pays étaient comprises entre −26% et 35%. Bien que la sensibilité moyenne des plateformes de l'OMS et de l'ECDC ne présente aucune différence (biais: 0%, intervalle de confiance de 95%: −2 à 2), la concordance en termes de sensibilité par organisme–pays était faible (limites de l'accord comprises entre −18 et 18%). Des variations considérables existent au niveau du nombre d'isolats entre les plateformes de l'OMS et de Pfizer (différence moyenne: 674, valeur p: < 0,001) et entre celles de l'OMS et de l'ECDC (différence moyenne: 192, valeur p: 0,04).

          Conclusion

          La grande hétérogénéité des données soumises par les pays à des plateformes de surveillance de la résistance aux antimicrobiens couramment utilisées compromet leur validité, et par conséquent les stratégies locales et mondiales de lutte contre cette résistance. Nous devons donc comprendre et résoudre ce manque de régularité des plateformes de surveillance ainsi que ses mécanismes sous-jacents.

          Resumen

          Objetivo

          Analizar cómo difieren los datos nacionales sobre susceptibilidad antimicrobiana utilizados para conformar los planes nacionales de acción, entre las diferentes plataformas de vigilancia.

          Métodos Se i

          dentificaron las plataformas de vigilancia disponibles, de libre acceso, supranacionales e interactivas, y se llevó a cabo una comprobación cruzada de sus datos, de conformidad con el Control de calidad de datos: módulo 1 de la Organización Mundial de la Salud (OMS). Se realizó una comparación entre la utilidad de la plataforma y la exhaustividad de los datos, coincidentes en el tiempo y relativos a la susceptibilidad antimicrobiana de cuatro tipos de bacterias sanguíneas aisladas: Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus y Streptococcus pneumoniae, información procedente del Sistema Mundial de Vigilancia de la Resistencia a los Antimicrobianos de la OMS; de la red del Centro Europeo de Control de Enfermedades (ECDC) y de la base de datos de Liderazgo y Vigilancia de Pruebas Antimicrobianas de Pfizer. Utilizando el análisis de Bland-Altman, las pruebas t pareadas y las pruebas de los rangos con signo de Wilcoxon, se analizaron los datos sobre susceptibilidad y el número de concordancias aisladas entre plataformas.

          Resultados

          De los 71 países que envían datos de manera activa a la OMS, 28 también los remiten a la base de datos de Pfizer; 19 al ECDC; y 16 a las tres plataformas. Los límites de concordancia entre las plataformas de la OMS y de Pfizer acerca de los datos sobre susceptibilidad organismo-país oscilaban entre el -26% y el 35%. Mientras que las susceptibilidades promedio entre las plataformas de la OMS y del ECDC no variaban (sesgo: 0%, intervalo de confianza del 95%: -2 a 2), la concordancia entre la susceptibilidad organismo-país era baja (límites de concordancia de -18 a 18%). Existen diferencias significativas en cifras aisladas notificadas por las plataformas de la OMS-Pfizer (media de la diferencia: 674, valor de p: < 0,001 y por las plataformas de la OMS–ECDC (media de la diferencia: 192, valor de p: 0,04).

          Conclusión

          La notable heterogeneidad de los datos enviados a nivel nacional a las plataformas de vigilancia de la resistencia antimicrobiana utilizadas comúnmente compromete su validez, menoscabándose así las estrategias locales y mundiales sobre la resistencia antimicrobiana. Por tanto, se debe comprender y abordar la variabilidad de las plataformas de vigilancia, así como sus mecanismos subyacentes.

          ملخص

          الغرض

          تقييم مدى اختلاف البيانات الوطنية للحساسية لمضادات الميكروبات المستخدمة لتوعية خطط العمل الوطنية عبر منصات المراقبة.

          الطريقة

          قمنا بتحديد منصات المراقبة التفاعلية المتاحة ذات الوصول المفتوح وعبر الوطنية، ودققنا بياناتها وفقًا لضمان جودة البيانات الخاص بمنظمة الصحة العالمية (WHO): الوحدة رقم 1. قمنا بمقارنة قابلية استخدام المنصة واكتمال البيانات المتطابقة مع الوقت عن حساسية مضادات الميكروبات بالنسبة لأربعة أنواع من عزل الدم: Escherichia coli ، و Klebsiella pneumoniae ، و Staphylococcus aureus و Streptococcus pneumoniae من النظام العالمي التابع لمنظمة الصحة العالمية لمقاومة مضادات الميكروبات ومراقبة استخدامها، وشبكة المركز الأوروبي لمكافحة الأمراض (ECDC)، وقاعدة بيانات فايزر لقيادة ومراقبة اختبارات مضادات الميكروبات. باستخدام تحليل Bland-Altman واختبارات t المزدوجة، واختبارات تصنيف Wilcoxon الموقّع، قمنا بتقييم بيانات الحساسية، وعدد التوافقات العزل بين المنصات.

          النتائج

          من بين 71 دولة تقدم البيانات بنشاط لمنظمة الصحة العالمية، تقدم 28 دولة منها أيضًا بيانات لقاعدة بيانات فايزر؛ و19 دولة تقدم البيانات لشبكة المركز الأوروبي لمكافحة الأمراض؛ وتقدم 16 دولة البيانات لجميع المنصات الثلاث. إن حدود التوافق بين بيانات منصتي منظمة الصحة العالمية وفايزر بشأن الحساسية المتعلقة بالكائنات الحية والدولة، تراوحت من %26 إلى %35. بينما لم تختلف الحساسيات المتوسطة لمنصتي منظمة الصحة العالمية، وشبكة المركز الأوروبي لمكافحة الأمراض (التحيز: %0، بفاصل الثقة مقداره 95: -2 إلى 2) كان التوافق بين حساسية الكائنات الحية والدولة منخفضًا (حدود التوافق −18 إلى %18). توجد فروق ذات دلالة إحصائية في أعداد العزل المبلغ عنها بين منظمة الصحة العالمية وفايزر (متوسط الاختلاف: 674، قيمة نسبة الاحتمال: أقل من 0.001 ومنصتي منظمة الصحة العالمية وشبكة المركز الأوروبي لمكافحة الأمراض (متوسط الاختلاف: 192، قيمة نسبة الاحتمال: 0.04).

          الاستنتاج

          إن عدم التجانس الهائل في البيانات الوطنية المقدمة لمنصات مراقبة مقاومة مضادات الميكروبات شائعة الاستخدام يضعف صلاحيتها، وبالتالي يقوض استراتيجيات مقاومة مضادات الميكروبات المحلية والعالمية. وبالتالي، فنحن بحاجة إلى فهم تنوع منصة المراقبة، والآليات الكامنة وراءها، والتعامل مع ذلك كله.

          摘要

          目的

          旨在评估在不同监测平台上用于指导国家行动计划的国家抗微生物药物敏感性数据的差异。

          方法

          根据世界卫生组织 (WHO) 数据质量保证:模块 1,我们确定了可用的开放获取、超国家的、交互式监测平台,并对其数据进行了交叉检查。我们比较了关于四种血液培养分离株的抗微生物药物敏感性的平台可用性和相同时间内的数据完整性,这四种分离株是:世卫组织全球抗微生物药物耐药性和使用监测系统、欧洲疾病预防控制中心 (ECDC) 网络和辉瑞抗微生物药物检测管理和监测数据库的 大肠杆菌、肺炎克雷伯菌、金黄色葡萄球菌和肺炎链球菌。使用 Bland-Altman 分析、配对 t-检验和威尔科克森符号秩检验,我们评估了不同平台之间的药物敏感性数据和分离株一致性的数量。

          结果

          在 71 个积极向世卫组织提交数据的国家中,有 28 个国家也向辉瑞的数据库提交了数据;19 个国家也向欧洲疾病预防控制中心提交了数据; 16 个国家向三个平台均提交了数据。世卫组织和辉瑞公司的国家生物药敏性数据平台之间的一致性差异范围为 -26% 至 35%。虽然世卫组织和欧洲疾病预防控制中心平台的平均敏感性没有差异(偏倚:0%;95% 置信区间:-2 至 2),但国家生物药物敏感性之间的一致性很低(一致性范围为 -18 至 18%)。世卫组织和辉瑞公司报告的分离株数量之间存在显著差异(差异均值:674, P-值:< 0.001,以及世卫组织和欧洲疾病预防控制中心平台之间存在的差异(差异均值:192, P-值:0.04)。

          结论

          各国向常用的抗微生物药物耐药性监测平台提交的数据存在相当大的异质性,影响了它们的有效性,从而对当地和全球抗微生物药物耐药性战略的制定产生了不利影响。因此,我们需要了解并解决监测平台的变化性及其潜在机制。

          Резюме

          Цель

          Оценить различия в национальных данных о восприимчивости к противомикробным препаратам, используемых для составления национальных планов действий, в зависимости от платформы эпиднадзора.

          Методы

          Были определены доступные надгосударственные интерактивные платформы эпиднадзора с открытым доступом, а также проведена перекрестная проверка их данных в соответствии с «Обеспечением качества данных» Всемирной организации здравоохранения (ВОЗ): модуль 1. Сравнивались удобство использования платформы и полнота сопоставленных по времени данных о чувствительности к противомикробным препаратам четырех видов, выделенных из крови: Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus и Streptococcus pneumonia e из Глобальной системы надзора за устойчивостью к противомикробным препаратам и их использованием ВОЗ, сети Европейского центра по контролю заболеваний (ECDC) и базы данных Pfizer – Antimicrobial Testing Leadership and Surveillance. Для оценки данных о восприимчивости и количества совпадений изолятов между платформами использовались: анализ Бланда-Альтмана, парные t-критерии и ранговые критерии Уилкоксона.

          Результаты

          Кроме того, данные из 71 страны, активно представляющей данные в ВОЗ, 28 стран также предоставляют данные в базу данных Pfizer, 19 – в ECDC, 16 – во все три платформы. Пределы согласия между платформами ВОЗ и Pfizer для данных о восприимчивости организмов по странам варьировались от –26 до 35%. Хотя средние значения восприимчивости платформ ВОЗ и ECDC не отличались (смещение: 0%, 95%-й ДИ: от –2 до 2), согласованность между восприимчивостью организма и страны была низкой (пределы согласования от –18 до 18%). Существуют значительные различия в количестве изолятов, зарегистрированных по данным ВОЗ-Pfizer (среднее значение разницы: 674, P-значение: < 0,001), и платформ ВОЗ-ECDC (среднее значение разницы: 192, P-значение: 0,04).

          Вывод

          Значительная неоднородность данных, представляемых странами в рамках общепринятых платформ эпиднадзора за устойчивостью к противомикробным препаратам, ставит под сомнение их достоверность, тем самым препятствуя реализации местных и глобальных стратегий борьбы с устойчивостью к противомикробным препаратам. Следовательно, возникает необходимость в понимании и решении проблемы изменчивости платформы наблюдения и лежащих в ее основе механизмов.

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          Most cited references14

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          • Abstract: found
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          Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

          (2022)
          Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.
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            Understanding Bland Altman analysis

            In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods.
In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement.
The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot.
The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals.
The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.
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              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Measuring and mapping the global burden of antimicrobial resistance

              The increasing number and global distribution of pathogens resistant to antimicrobial drugs is potentially one of the greatest threats to global health, leading to health crises arising from infections that were once easy to treat. Infections resistant to antimicrobial treatment frequently result in longer hospital stays, higher medical costs, and increased mortality. Despite the long-standing recognition of antimicrobial resistance (AMR) across many settings, there is surprisingly poor information about its geographical distribution over time and trends in its population prevalence and incidence. This makes reliable assessments of the health burden attributable to AMR difficult, weakening the evidence base to drive forward research and policy agendas to combat AMR. The inclusion of mortality and morbidity data related to drug-resistant infections into the annual Global Burden of Disease Study should help fill this policy void.
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                Author and article information

                Journal
                Bull World Health Organ
                Bull World Health Organ
                BLT
                Bulletin of the World Health Organization
                World Health Organization
                0042-9686
                1564-0604
                01 August 2023
                29 May 2023
                : 101
                : 8
                : 501-512F
                Affiliations
                [a ]deptCentre of Defence Pathology, Royal Centre for Defence Medicine , Queen Elizabeth Hospital Birmingham , Birmingham, , B15 2WB , England.
                [b ]deptCentre of Excellence in Infectious Diseases Research , University of Liverpool , Liverpool, , England.
                [c ]deptInfection and Immunity Clinical Academic Group , St George’s University Hospitals NHS Foundation Trust , London, , England.
                [d ]deptInstitute for Infection and Immunity , St George’s University of London , London, , England.
                [e ]deptMedicine at Sibley Memorial Hospital , Johns Hopkins University , Baltimore, , United States of America.
                [f ]deptClinical Infection Department , Chelsea and Westminster Hospital , London, , England.
                [g ]deptNational Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance , Imperial College London , London, , England.
                Author notes
                Correspondence to Scott JC Pallett (email: scott.pallett@ 123456nhs.net ).
                Article
                BLT.22.289403
                10.2471/BLT.22.289403
                10388141
                37529028
                55527093-9116-4260-a334-f4e0330502d4
                (c) 2023 The authors; licensee World Health Organization.

                This is an open access article distributed under the terms of the Creative Commons Attribution IGO License ( http://creativecommons.org/licenses/by/3.0/igo/legalcode), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WHO or this article endorse any specific organization or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.

                History
                : 14 November 2022
                : 21 March 2023
                : 11 April 2023
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                Research

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