Sewage surveillance for assessing clinical antibiotic resistance prevalence: Combining metagenomic and phenotypic data.

Carl-Fredrik Flach, Fanny Berglund, Gilbert Osena, Patricia M C Huijbers, D G Joakim Larsson

Abstract read

In one paragraph

Article in One health (Amsterdam, Netherlands), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Carl-Fredrik FlachCentre for Antibiotic Resistance Research in Gothenburg (CARe), University of Gothenburg, Gothenburg, Sweden.

Fanny BerglundCentre for Antibiotic Resistance Research in Gothenburg (CARe), University of Gothenburg, Gothenburg, Sweden.

Gilbert OsenaCentre for Antibiotic Resistance Research in Gothenburg (CARe), University of Gothenburg, Gothenburg, Sweden.

Patricia M C HuijbersCentre for Antibiotic Resistance Research in Gothenburg (CARe), University of Gothenburg, Gothenburg, Sweden.

D G Joakim LarssonCentre for Antibiotic Resistance Research in Gothenburg (CARe), University of Gothenburg, Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surveillance of antibiotic resistance in clinical isolates is a cornerstone for the management of bacterial infections but is limited in large parts of the world, often due to lack of resources. Sewage surveillance has been proposed as a promising, resource-efficient complement to the traditional surveillance approach based on samples from many individual patients. Both phenotypic data on resistance in sewage isolates and abundance of antibiotic resistance genes in sewage have been shown to correlate with resistance prevalence in clinical isolates. Here, we aimed to directly compare and combine an isolate-based and a gene-based sewage surveillance approach to evaluate what best can reflect clinical resistance rates. The two approaches, based on susceptibility testing of collected

Indexed as

Antibiotic resistance geneClinical isolatesEscherichia coliMetagenomicsResistance rateWastewater

Identifiers

PMID42381665

DOI10.1016/j.onehlt.2026.101485

PMCPMC13316215

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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.