Evidence map›Paper›PMID 39927312›Full record

ArticleJAC-antimicrobial resistance2025

Observational study of antibiotic prescribing patterns by age and sex in primary care in England: why we need to take this variation into account to evaluate antibiotic stewardship and predict AMR variation.

Naomi R Waterlow, Tom Ashfield, Gwenan M Knight

Abstract read
In one paragraph

Article in JAC-antimicrobial resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

3 authors.

Naomi R WaterlowCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0002-9210-9657
Tom AshfieldThe Signpost, Winchester, UK.
Gwenan M KnightCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.ORCID https://orcid.org/0000-0002-7263-9896

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The drivers of antimicrobial resistance (AMR) likely vary substantially by different demographics. However, few complete, open, national detailed data exist on how antibiotic use (ABU) varies by both age and sex. Here, we aimed to describe the variation in ABU and consider how these age- and sex-specific patterns influence targets and methods for AMR control. Methods: Prescriptions of all antibiotics from general practices in England for 2015-23 disaggregated by 5 year age bands and sex were analysed at the national and Integrated Care Board (ICB) level. A descriptive analysis of the relative rates of ABU by age and sex was conducted, followed by an evaluation of comparison metrics of prescription levels between regions. ABU by age and sex were compared with global AWaRe targets, and ABU by age-group was correlated against influenza vaccination introduction, to evaluate the impact of such vaccines on ABU. Results: From a total of 249 578 795 prescriptions (across 9 years), 63% were given to women and the most prescribed were amoxicillin, nitrofurantoin and flucloxacillin sodium. Prescriptions per 100 000 population varied substantially across sex, age, geographical region, season, year, COVID-19 pandemic period and drug. Most antibiotics were prescribed more to women across most age bands (84% of antibiotics had more prescriptions to females across 50% of age bands). We show how this variation requires a more nuanced approach to comparing ABU across geographies and highlight that AWaRe targets are not met uniformly [prescribing in men aged 11-20 does not fulfil the Access, Watch and Reserve (AWaRe) 80% Access target]. We also show the impact on ABU of time-sensitive interruptions (38% of antibiotics showed a seasonal pattern in the absence of disruptions), including differential age-targeted influenza vaccination, COVID-19 restrictions and a shortage of amoxicillin due to a group A Conclusions: These detailed differences in ABU across England suggest that there should be large variation in AMR burden by age and sex. Linkage of this ABU data with similarly detailed open-access AMR data is now needed for better intervention design.

Identifiers

PMID39927312
PMCPMC11803082

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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.