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[Background] Lack of specific definitions of clinical characteristics, disease severity, and risk and preventive factors of post-COVID-19 syndrome (PCS) severely impacts research and discovery of new preventive and therapeutics drugs., [Methods] This prospective multicenter cohort study was conducted from February 2020 to June 2022 in 5 countries, enrolling SARS-CoV-2 out- and in-patients followed at 3-, 6-, and 12-month from diagnosis, with assessment of clinical and biochemical features, antibody (Ab) response, Variant of Concern (VoC), and physical and mental quality of life (QoL). Outcome of interest was identification of risk and protective factors of PCS by clinical phenotype, setting, severity of disease, treatment, and vaccination status. We used SF-36 questionnaire to assess evolution in QoL index during follow-up and unsupervised machine learning algorithms (principal component analysis, PCA) to explore symptom clusters. Severity of PCS was defined by clinical phenotype and QoL. We also used generalized linear models to analyse the impact of PCS on QoL and associated risk and preventive factors. CT registration number: NCT05097677., [Findings] Among 1796 patients enrolled, 1030 (57%) suffered from at least one symptom at 12-month. PCA identified 4 clinical phenotypes: chronic fatigue-like syndrome (CFs: fatigue, headache and memory loss, 757 patients, 42%), respiratory syndrome (REs: cough and dyspnoea, 502, 23%); chronic pain syndrome (CPs: arthralgia and myalgia, 399, 22%); and neurosensorial syndrome (NSs: alteration in taste and smell, 197, 11%). Determinants of clinical phenotypes were different (all comparisons p < 0.05): being female increased risk of CPs, NSs, and CFs; chronic pulmonary diseases of REs; neurological symptoms at SARS-CoV-2 diagnosis of REs, NSs, and CFs; oxygen therapy of CFs and REs; and gastrointestinal symptoms at SARS-CoV-2 diagnosis of CFs. Early treatment of SARS-CoV-2 infection with monoclonal Ab (all clinical phenotypes), corticosteroids therapy for mild/severe cases (NSs), and SARS-CoV-2 vaccination (CPs) were less likely to be associated to PCS (all comparisons p < 0.05). Highest reduction in QoL was detected in REs and CPs (43.57 and 43.86 vs 57.32 in PCS-negative controls, p < 0.001). Female sex (p < 0.001), gastrointestinal symptoms (p = 0.034) and renal complications (p = 0.002) during the acute infection were likely to increase risk of severe PCS (QoL <50). Vaccination and early treatment with monoclonal Ab reduced the risk of severe PCS (p = 0.01 and p = 0.03, respectively)., [Interpretation] Our study provides new evidence suggesting that PCS can be classified by clinical phenotypes with different impact on QoL, underlying possible different pathogenic mechanisms. We identified factors associated to each clinical phenotype and to severe PCS. These results might help in designing pathogenesis studies and in selecting high-risk patients for inclusion in therapeutic and management clinical trials.

Background Variation in the approaches taken to contain the SARS-CoV-2 (COVID-19) pandemic at country level has been shaped by economic and political considerations, technical capacity, and assumptions about public behaviours. To address the limited application of learning from previous pandemics, this study aimed to analyse perceived facilitators and inhibitors during the pandemic and to inform the development of an assessment tool for pandemic response planning. Methods A cross-sectional electronic survey of health and non-health care professionals (5 May - 5 June 2020) in six languages, with respondents recruited via email, social media and website posting. Participants were asked to score inhibitors (-10 to 0) or facilitators (0 to +10) impacting country response to COVID-19 from the following domains - Political, Economic, Sociological, Technological, Ecological, Legislative, and wider Industry (the PESTELI framework). Participants were then asked to explain their responses using free text. Descriptive and thematic analysis was followed by triangulation with the literature and expert validation to develop the assessment tool, which was then compared with four existing pandemic planning frameworks. Results 928 respondents from 66 countries (57% health care professionals) participated. Political and economic influences were consistently perceived as powerful negative forces and technology as a facilitator across high- and low-income countries. The 103-item tool developed for guiding rapid situational assessment for pandemic planning is comprehensive when compared to existing tools and highlights the interconnectedness of the 7 domains. Conclusions The tool developed and proposed addresses the problems associated with decision making in disciplinary silos and offers a means to refine future use of epidemic modelling.

Objectives/purpose: The costs attributable to antimicrobial resistance (AMR) remain theoretical and largely unspecified. Current figures fail to capture the full health and economic burden caused by AMR across human, animal, and environmental health; historically many studies have considered only direct costs associated with human infection from a hospital perspective, primarily from high-income countries. The Global Antimicrobial Resistance Platform for ONE-Burden Estimates (GAP-ON€) network has developed a framework to help guide AMR costing exercises in any part of the world as a first step towards more comprehensive analyses for comparing AMR interventions at the local level as well as more harmonized analyses for quantifying the full economic burden attributable to AMR at the global level. Methods: GAP-ON€ (funded under the JPIAMR 8th call (Virtual Research Institute) is composed of 19 international networks and institutions active in the field of AMR. For this project, the Network operated by means of Delphi rounds, teleconferences and face-to-face meetings. The resulting costing framework takes a bottom-up approach to incorporate all relevant costs imposed by an AMR bacterial microbe in a patient, in an animal, or in the environment up through to the societal level. Results: The framework itemizes the epidemiological data as well as the direct and indirect cost components needed to build a realistic cost picture for AMR. While the framework lists a large number of relevant pathogens for which this framework could be used to explore the costs, the framework is sufficiently generic to facilitate the costing of other resistant pathogens, including those of other aetiologies. Conclusion: In order to conduct cost-effectiveness analyses to choose amongst different AMR-related interventions at local level, the costing of AMR should be done according to local epidemiological priorities and local health service norms. Yet the use of a common framework across settin

[Objectives/purpose] The costs attributable to antimicrobial resistance (AMR) remain theoretical and largely unspecified. Current figures fail to capture the full health and economic burden caused by AMR across human, animal, and environmental health; historically many studies have considered only direct costs associated with human infection from a hospital perspective, primarily from high-income countries. The Global Antimicrobial Resistance Platform for ONE-Burden Estimates (GAP-ON€) network has developed a framework to help guide AMR costing exercises in any part of the world as a first step towards more comprehensive analyses for comparing AMR interventions at the local level as well as more harmonized analyses for quantifying the full economic burden attributable to AMR at the global level., [Methods] GAP-ON€ (funded under the JPIAMR 8th call (Virtual Research Institute) is composed of 19 international networks and institutions active in the field of AMR. For this project, the Network operated by means of Delphi rounds, teleconferences and face-to-face meetings. The resulting costing framework takes a bottom-up approach to incorporate all relevant costs imposed by an AMR bacterial microbe in a patient, in an animal, or in the environment up through to the societal level., [Results] The framework itemizes the epidemiological data as well as the direct and indirect cost components needed to build a realistic cost picture for AMR. While the framework lists a large number of relevant pathogens for which this framework could be used to explore the costs, the framework is sufficiently generic to facilitate the costing of other resistant pathogens, including those of other aetiologies., [Conclusion] In order to conduct cost-effectiveness analyses to choose amongst different AMR-related interventions at local level, the costing of AMR should be done according to local epidemiological priorities and local health service norms. Yet the use of a common framework across settings allows for the results of such studies to contribute to cumulative estimates that can serve as the basis of broader policy decisions at the international level such as how to steer R&D funding and how to prioritize AMR amongst other issues. Indeed, it is only by building a realistic cost picture that we can make informed decisions on how best to tackle major health threats.

Objectives/purpose: The costs attributable to antimicrobial resistance (AMR) remain theoretical and largely unspecified. Current figures fail to capture the full health and economic burden caused by AMR across human, animal, and environmental health; historically many studies have considered only direct costs associated with human infection from a hospital perspective, primarily from high-income countries. The Global Antimicrobial Resistance Platform for ONE-Burden Estimates (GAP-ON€) network has developed a framework to help guide AMR costing exercises in any part of the world as a first step towards more comprehensive analyses for comparing AMR interventions at the local level as well as more harmonized analyses for quantifying the full economic burden attributable to AMR at the global level. Methods: GAP-ON€ (funded under the JPIAMR 8th call (Virtual Research Institute) is composed of 19 international networks and institutions active in the field of AMR. For this project, the Network operated by means of Delphi rounds, teleconferences and face-to-face meetings. The resulting costing framework takes a bottom-up approach to incorporate all relevant costs imposed by an AMR bacterial microbe in a patient, in an animal, or in the environment up through to the societal level. Results: The framework itemizes the epidemiological data as well as the direct and indirect cost components needed to build a realistic cost picture for AMR. While the framework lists a large number of relevant pathogens for which this framework could be used to explore the costs, the framework is sufficiently generic to facilitate the costing of other resistant pathogens, including those of other aetiologies. Conclusion: In order to conduct cost-effectiveness analyses to choose amongst different AMR-related interventions at local level, the costing of AMR should be done according to local epidemiological priorities and local health service norms. Yet the use of a common framework across settin