Evidence map›Paper›PMID 41023856›Full record

ArticleBMC infectious diseases2025

Climate change and antimicrobial resistance: a global-scale analysis.

Yaqin Ni, Jin Zhao, Yuhua Yuan, Baihuan Feng

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Infectious diseases in a changing climate.Nature reviews. Microbiology · 2026
    Article
  3. Article
  4. Occurrence of Phenotypic Multidrug-ResistantAntibiotics (Basel, Switzerland) · 2026
    Article
  5. Temperature-Associated Prevalence and Multidrug Resistance ofAnimals : an open access journal from MDPI · 2026
    Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
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

4 authors.

Yaqin Ni *Department of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Jin Zhao *Department of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Yuhua YuanDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China.
Baihuan FengDepartment of Infection Prevention and Control, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 Qingchun East Road, Shangcheng District, Hangzhou, Zhejiang, 310016, China. fengbaihuan@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntimicrobial resistance (AMR) represents a major global health threat. Although regional studies have explored the relationship between climate change and AMR, a comprehensive global analysis incorporating extreme climate events has not yet been conducted.

methodsWe analyzed global data from 2000 to 2023, encompassing over 28 million bacterial isolates from eight common pathogens and 14 antibiotic categories. Climate data were sourced from NOAA, and resistance data were obtained from ResistanceMap, ECDC, and PLISA databases. Linear mixed-effects models (LMMs) were applied to evaluate the associations between climate indices and resistance rates.

resultsTemperature was consistently positively correlated with resistance rates across most bacterial species. The mean temperature was significantly associated with resistance rates even after adjusting for covariates. Extreme temperature indicators, including intensity indices (TXx, TNx, TXn and TNn), absolute threshold indices (SU, TR and DTR), relative threshold indices (TN90p and TX90p), and duration indices (CSDI and WSDI) exhibited significant positive correlations with resistance rates. In contrast, cold-related indices (FD, ID, TN10p and TX10p) were negatively correlated with resistance rates. Among the precipitation indices, only CDD demonstrated a significant positive association with aggregated AMR after full adjustment; all the other precipitation metrics showed no statistically significant correlation. Furthermore, subgroup analyses of WHO priority pathogens confirmed the robust effect of temperature, but revealed that precipitation indices, particularly CDD, had opposing correlations with resistance across different pathogens.

conclusionsThis study provides robust global evidence that rising temperatures and extreme heat are consistent drivers of AMR, whereas the impact of precipitation is complex and pathogen dependent. These findings underscore the need for climate-informed public health strategies that integrate climate surveillance into AMR action plans to develop targeted interventions against these intertwined global threats.

Indexed as

Anti-Bacterial AgentsBacteriaClimate ChangeDrug Resistance, BacterialGlobal HealthHumansTemperatureAnti-Bacterial AgentsAntimicrobial resistanceClimate changeExpert team on climate change detection and indices

Identifiers

PMID41023856
PMCPMC12482477

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.