Evidence map›Paper›PMID 42401555›Full record

ReviewNature communications2026

The burden of antimicrobial-resistant bacterial infections: a causal perspective.

Cherry Lim, Koen B Pouwels, Sue J Lee, Yin Mo, Rogier H van Doorn, Paul Turner, Ben S Cooper

Abstract readReview
In one paragraph

Review in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Cherry LimCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. cherry.lim@ndm.ox.ac.uk.ORCID 0000-0003-2555-6980
Koen B PouwelsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID 0000-0001-7097-8950
Sue J LeeCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0002-8409-4248
Yin MoCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0002-0216-9550
Rogier H van DoornCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0002-9807-1821
Paul TurnerCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0002-1013-7815
Ben S CooperCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. ben.cooper@ndm.ox.ac.uk.ORCID 0000-0002-9445-7217

Funding

Wellcome Trust (Wellcome) 223606/Z/21/Z
6 · The paper itself

Abstract

Estimates of preventable antimicrobial resistance (AMR) burden are important to inform local, national, regional, and global policies, targets and research priorities. Such estimates rely heavily on model assumptions and several analytical approaches have been used. In this perspective article, we outline key conceptual and practical challenges in estimating AMR burden, and propose strategies for building on existing work to obtain more policy-relevant burden estimates. We highlight how new approaches taking an explicitly causal perspective are tackling these problems and have the potential to improve the way results are combined from individual studies to estimate national and regional AMR burden.

Indexed as

Anti-Bacterial AgentsBacterial InfectionsDrug Resistance, BacterialBacteriaHumansAnti-Bacterial Agents

Identifiers

PMID42401555
PMCPMC13333017

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.