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

Antimicrobial Communicable diseases Computational Epidemiology Mathematical Models Resistance Transmission

Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
24 04 2019
Historique:
received: 17 12 2018
accepted: 25 03 2019
entrez: 25 4 2019
pubmed: 25 4 2019
medline: 14 11 2019
Statut: epublish

Résumé

Mathematical 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. We 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. We 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. The 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.

Sections du résumé

BACKGROUND
Mathematical 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.
METHODS
We 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.
RESULTS
We 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.
CONCLUSIONS
The 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.

Identifiants

pubmed: 31014341
doi: 10.1186/s12916-019-1314-9
pii: 10.1186/s12916-019-1314-9
pmc: PMC6480522
doi:

Substances chimiques

Anti-Bacterial Agents 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

81

Subventions

Organisme : NICHD NIH HHS
ID : P2C HD047879
Pays : United States

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Auteurs

Anna Maria Niewiadomska (AM)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.

Bamini Jayabalasingham (B)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Present Address: Elsevier Inc., 230 Park Ave, Suite B00, New York, NY, 10169, USA.

Jessica C Seidman (JC)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.

Lander Willem (L)

University of Antwerp, Antwerp, Belgium.

Bryan Grenfell (B)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.
Princeton University, Princeton, NJ, USA.

David Spiro (D)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA.

Cecile Viboud (C)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, USA. viboudc@mail.nih.gov.

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