Public funding for research on antibacterial resistance in the JPIAMR countries, the European Commission, and related European Union agencies: a systematic observational analysis
Bibliographic record
Abstract
BACKGROUND: Antibacterial resistant infections are rising continuously, resulting in increased morbidity and mortality worldwide. With no new antibiotic classes entering the market and the possibility of returning to the pre-antibiotic era, the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) was established to address this problem. We aimed to quantify the scale and scope of publicly funded antibacterial resistance research across JPIAMR countries and at the European Union (EU) level to identify gaps and future opportunities. METHODS: We did a systematic observational analysis examining antibacterial resistance research funding. Databases of funding organisations across 19 countries and at EU level were systematically searched for publicly funded antibacterial resistance research from Jan 1, 2007, to Dec 31, 2013. We categorised studies on the basis of the JPIAMR strategic research agenda's six priority topics (therapeutics, diagnostics, surveillance, transmission, environment, and interventions) and did an observational analysis. Only research funded by public funding bodies was collected and no private organisations were contacted for their investments. Projects in basic, applied, and clinical research, including epidemiological, public health, and veterinary research and trials were identified using keyword searches by organisations, and inclusion criteria were based on the JPIAMR strategic research agenda's six priority topics, using project titles and abstracts as filters. FINDINGS: We identified 1243 antibacterial resistance research projects, with a total public investment of €1·3 billion across 19 countries and at EU level, including public investment in the Innovative Medicines Initiative. Of the total amount invested in antibacterial resistance research across the time period, €646·6 million (49·5%) was invested at the national level and €659·2 million (50·5%) at the EU level. When projects were classified under the six priority topics we found that 763 (63%) of 1208 projects funded at national level were within the area of therapeutics, versus 185 (15%) in transmission, 131 (11%) in diagnostics, 53 (4%) in interventions, and only 37 (3%) in environment and 39 (3%) in surveillance. INTERPRETATION: This was the first systematic analysis of research funding of antibacterial resistance of this scale and scope, which relied on the availability and accuracy of data from organisations included. Large variation was seen between countries both in terms of number of projects and associated investment and across the six priority topics. To determine the future direction of JPIAMR countries a clear picture of the funding landscape across Europe and Canada is needed. Countries should work together to increase the effect of research funding by strengthening national and international coordination and collaborations, harmonising research activities, and collectively pooling resources to fund multidisciplinary projects. The JPIAMR have developed a publicly available database to document the antibacterial resistance research collected and can be used as a baseline to analyse funding from 2014 onwards. FUNDING: JPIAMR and the European Commission.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.034 | 0.053 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".