Modelling the impact of monoclonal antibodies and vaccines on the reduction of antimicrobial resistance

Topic description

Specific Challenge:

Vaccines and monoclonal Antibodies (mAb) may reduce antimicrobial resistance (AMR). However, individual vaccine developers and manufacturers, as well as organisations developing mAbs and health authorities, acting alone, do not have the resources and the full expertise required to make a realistic and comparable assessment of the use of the different products on the reduction of AMR. This could instead be possible through the development of a mathematical model. For such a model to be representative of the concerns and interests of the various actors (i.e. industry and the public health sector), it should take into account the perspectives of these different actors in order to capture all relevant impacts both in terms of costs and health outcomes.

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The proposal has the following scope:

1) Evaluate the burden of disease of AMR by estimating inpatients’ (acute care hospitals and long-term care facilities) and outpatients’ infection rates in at least 8 EU countries for which suitable data is collected and available, as well as in the US , and the relative attributable risk for morbidity, mortality and costs.

2) Build a comprehensive AMR model (i.e. model structure, parameters, assumptions) based on an analysis of the strengths and weaknesses of existing models, and a gap analysis.

3) Collecting, gathering, and analysing data from existing databases to feed the model.

4) Develop and test a cost-effectiveness analysis (CEA) to estimate the cost and benefits of covering defined target groups (e.g. 18+, 60+, surgeries) with mAbs and vaccines.

5) Set up a study to test, monitor, evaluate and improve the model.

6) Ensure a public and broad access to the model

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The epidemiological repository that will be obtained by the applicant consortium, besides providing a transparent basis for the BOD estimation, will be made accessible through an internet database to be designed within the project. Any researcher will benefit from using the most comprehensive database on the epidemiology of infectious diseases and resource consumption associated with sensitive and resistant pathogens. Producing a reliable repository with clear description of the methods used to derive the estimates of the BOD and AMR will benefit the credibility of the results of the mathematical model. During the project, the access will be free of charge. The ambition is to favour open access as much as possible.

The results of the mathematical model (publicly available and free of charge) will allow policymakers and healthcare managers to make informed decisions on vaccines and mAb strategies. The impact will include clear direction for EFPIA partners and health care authorities on which research and development strategies should be prioritised to reduce AMR through vaccine and monoclonal antibodies.

Better chance of preserving the efficacy of last-resort antimicrobials. As an example, the European Centre for Disease Prevention and Control has published guidelines for the screening of patients at high risk for Carbapenem Resistant Enerobacteraceae (CRE) and Carbapenemase Producing Enterobacteriaceae (CPE) at the time of admission. The retrospective record review will provide an assessment on their status of implementation and will allow estimation of the resources required to put in place a functional screening and surveillance system for CRE and CPE, as well as other types of resistance.

Testing the sustainability of the study approach by financing a multi-year monitoring and evaluation system in key health units of a few pilot countries. The impact will be a strengthening of the existing AMR surveillance systems, and a verification of the assumptions and parameters underlying the model. For example, an initial model focused on a specific vaccine or mAb might provide an initial base which will be fine-tuned according to real data, and which will be further expanded to other promising mAbs and vaccinations, with further fine-tuning.

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