Evidence map›Paper›PMID 31014341›Full record

SynthesisBMC medicine2019

Population-level mathematical modeling of antimicrobial resistance: a systematic review.

Anna Maria Niewiadomska, Bamini Jayabalasingham, Jessica C Seidman, Lander Willem, Bryan Grenfell, David Spiro, Cecile Viboud

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in BMC medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
5.9field-weighted citation impact, top 3% of its field
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

40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 96 citations in OpenAlex.

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  19. Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review.IEEE access : practical innovations, open solutions · 2024
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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

7 authors at 4 institutions in 2 countries.

Anna Maria NiewiadomskaDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Bamini JayabalasinghamDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Jessica C SeidmanDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Lander WillemUniversity of Antwerp, Antwerp, Belgium.
Bryan GrenfellDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
David SpiroDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Cecile ViboudDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA. viboudc@mail.nih.gov.
Fogarty International Center · USNational Institutes of Health · USPrinceton University · USUniversity of Antwerp · BE

Funding

Scientific and Technical Computing CoreP2CHD047879 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2014 to 2018
$2.0M
NICHD NIH HHS P2C HD047879
6 · The paper itself

Abstract

backgroundMathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

methodsWe performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

resultsWe identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

conclusionsThe AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Indexed as

Anti-Bacterial AgentsDrug Resistance, BacterialHumansModels, TheoreticalAnti-Bacterial AgentsAntimicrobialCommunicable diseasesComputationalEpidemiologyMathematicalModelsResistanceTransmission

Identifiers

PMID31014341
PMCPMC6480522
OpenAlexW2941479653

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.