Evidence map›Paper›PMID 30116525›Full record

ArticleAntimicrobial resistance and infection control2018

Enumerating the economic cost of antimicrobial resistance per antibiotic consumed to inform the evaluation of interventions affecting their use.

Poojan Shrestha, Ben S Cooper, Joanna Coast, Raymond Oppong, Nga Do Thi Thuy, Tuangrat Phodha, Olivier Celhay, Philippe J Guerin, Heiman Wertheim, Yoel Lubell

Abstract read
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In one paragraph

Article in Antimicrobial resistance and infection control, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
134citing papers in PubMed, 6 pooled it
–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

134 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  7. Cost-effectiveness analysis of procalcitonin and lung ultrasonography guided antibiotic prescriptions in primary care.The European journal of health economics : HEPAC : health economics in prevention and care · 2025
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  19. Unlocking the potential ofFrontiers in microbiology · 2026
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74 more citing papers are in PubMed but not listed here.

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

10 authors.

Poojan Shrestha1Infectious Diseases Data Observatory, University of Oxford, Oxford, UK.
Ben S Cooper2Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Joanna Coast4School of Social and Community Medicine, University of Bristol, Bristol, UK.
Raymond Oppong5Health Economics Unit, School of Health and Population Sciences, University of Birmingham, Birmingham, UK.
Nga Do Thi ThuyOxford University Clinical Research Unit-Ha Noi, Ha Noi, Vietnam.
Tuangrat Phodha8Faculty of Pharmacy, Mahidol University, Bangkok, Thailand.
Olivier Celhay3Mahidol Oxford Tropical Medicine Research Unit Faculty of Tropical Medicine, Mahidol University, 420/6 Rajvithi Road, Bangkok, 10400 Thailand.
Philippe J Guerin1Infectious Diseases Data Observatory, University of Oxford, Oxford, UK.
Heiman WertheimOxford University Clinical Research Unit-Ha Noi, Ha Noi, Vietnam.
Yoel Lubell2Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Funding

Medical Research Council MR/K006924/1Wellcome TrustWellcome Trust 106698/Z/14/Z
6 · The paper itself

Abstract

Background: Antimicrobial resistance (AMR) poses a colossal threat to global health and incurs high economic costs to society. Economic evaluations of antimicrobials and interventions such as diagnostics and vaccines that affect their consumption rarely include the costs of AMR, resulting in sub-optimal policy recommendations. We estimate the economic cost of AMR per antibiotic consumed, stratified by drug class and national income level. Methods: The model is comprised of three components: correlation coefficients between human antibiotic consumption and subsequent resistance; the economic costs of AMR for five key pathogens; and consumption data for antibiotic classes driving resistance in these organisms. These were used to calculate the economic cost of AMR per antibiotic consumed for different drug classes, using data from Thailand and the United States (US) to represent low/middle and high-income countries. Results: The correlation coefficients between consumption of antibiotics that drive resistance in Conclusion: The economic costs of AMR per antibiotic consumed were considerable, often exceeding their purchase cost. Differences between Thailand and the US were apparent, corresponding with variation in the overall burden of AMR and relative prevalence of different pathogens. Notwithstanding their limitations, use of these estimates in economic evaluations can make better-informed policy recommendations regarding interventions that affect antimicrobial consumption and those aimed specifically at reducing the burden of AMR.

Indexed as

Drug Resistance, BacterialAnti-Bacterial AgentsBacteriaBacterial InfectionsCarbapenemsDrug UtilizationHumansMacrolidesQuinolonesThailandUnited StatesAnti-Bacterial AgentsCarbapenemsMacrolidesQuinolonesAntibiotic resistanceAntimicrobial resistanceCost of resistanceEconomic costEconomic evaluations

Identifiers

PMID30116525
PMCPMC6085682

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.