Dataset Information

Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level.

ABSTRACT: BackgroundThe spread of antimicrobial resistance (AMR) is of worldwide concern. Public health policymakers and pharmaceutical companies pursuing antibiotic development require accurate predictions about the future spread of AMR.AimWe aimed to identify and model temporal and geographical patterns of AMR spread and to predict future trends based on a slow, intermediate or rapid rise in resistance.MethodsWe obtained data from five antibiotic resistance surveillance projects spanning the years 1997 to 2015. We aggregated the isolate-level or country-level data by country and year to produce country-bacterium-antibiotic class triads. We fitted both linear and sigmoid models to these triads and chose the one with the better fit. For triads that conformed to a sigmoid model, we classified AMR progression into one of three characterising paces: slow, intermediate or fast, based on the sigmoid slope. Within each pace category, average sigmoid models were calculated and validated.ResultsWe constructed a database with 51,670 country-year-bacterium-antibiotic observations, grouped into 7,440 country-bacterium-antibiotic triads. A total of 1,037 triads (14%) met the inclusion criteria. Of these, 326 (31.4%) followed a sigmoid (logistic) pattern over time. Among 107 triads for which both sigmoid and linear models could be fit, the sigmoid model was a better fit in 84%. The sigmoid model deviated from observed data by a median of 6.5%; the degree of deviation was related to the pace of spread.ConclusionWe present a novel method of describing and predicting the spread of antibiotic-resistant organisms.

SUBMITTER: Fallach N

PROVIDER: S-EPMC7403637 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level.

Fallach Noga N Dickstein Yaakov Y Silberschein Erez E Turnidge John J Temkin Elizabeth E Almagor Jonatan J Carmeli Yehuda Y

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 20200601 23

BackgroundThe spread of antimicrobial resistance (AMR) is of worldwide concern. Public health policymakers and pharmaceutical companies pursuing antibiotic development require accurate predictions about the future spread of AMR.AimWe aimed to identify and model temporal and geographical patterns of AMR spread and to predict future trends based on a slow, intermediate or rapid rise in resistance.MethodsWe obtained data from five antibiotic resistance surveillance projects spanning the years 1997 ...[more]

PMID: 32553060

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Project description:BackgroundInternational travel has been recognized as a risk factor contributing to the spread of antimicrobial resistance (AMR). However, tools focused on AMR in the context of international travel and designed to guide decision-making are limited. We aimed at developing an evidence-based educational tool targeting both healthcare professionals (HCPs) and international travellers to help prevent the spread of AMR.MethodsA literature review on 12 antimicrobial-resistant bacteria (ARB) listed as critical and high tiers in the WHO Pathogen Priority List covering four key areas was carried out: AMR surveillance data; epidemiological studies reporting ARB prevalence data on carriage in returning travellers; guidance documents reporting indications on screening for ARB in returning travellers and recommendations for ARB prevention for the public. The evidence, catalogued at country-level, provided the content for a series of visualizations that allow assessment of the risk of AMR acquisition through travel.ResultsUp to January 2021, the database includes data on: (i) AMR surveillance for 2.018.241 isolates from 86 countries; (ii) ARB prevalence of carriage from 11.679 international travellers and (iii) 15 guidance documents published by major public health agencies. The evidence allowed the development of a consultation scheme for the evaluation of risk factors, prevalence of carriage, proportion and recommendations for screening of AMR. For the public, pre-travel practical measures to minimize the risk of transmission were framed.ConclusionsThis easy-to-use, annually updated, freely accessible AMR travel tool (https://epi-net.eu/travel-tool/overview/), is the first of its kind to be developed. For HCPs, it can provide a valuable resource for teaching and a repository that facilitates a stepwise assessment of the risk of AMR spread and strengthen implementation of optimized infection control measures. Similarly, for travellers, the tool has the potential to raise awareness of AMR and outlines preventive measures that reduce the risk of AMR acquisition and spread.

Project description: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.

Dataset Information

Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level.

Publications

Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level.

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