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Understanding the Impact of Interventions to Prevent Antimicrobial Resistant Infections in the Long-Term Care Facility: A Review and Practical Guide to Mathematical Modeling

Published online by Cambridge University Press:  19 December 2016

Alicia Rosello ,
Carolyne Horner ,
Susan Hopkins ,
Andrew C. Hayward  and
Sarah R. Deeny
Alicia Rosello*
Affiliation:
Institute of Health Informatics, Farr Institute of Health Informatics Research, UCL, London, United Kingdom Modelling and Economics Unit, National Infection Service, Public Health England, London, United Kingdom
Carolyne Horner
Affiliation:
Regional Laboratory Leeds, Public Health England, Leeds, United Kingdom
Susan Hopkins
Affiliation:
Healthcare Associated Infections Surveillance, National Infection Service, Public Health England, London, United Kingdom Department of Infectious Diseases and Microbiology, Royal Free London NHS Foundation Trust, London, United Kingdom
Andrew C. Hayward
Affiliation:
Institute of Health Informatics, Farr Institute of Health Informatics Research, UCL, London, United Kingdom
Sarah R. Deeny
Affiliation:
Modelling and Economics Unit, National Infection Service, Public Health England, London, United Kingdom Data analytics, The Health Foundation, London, United Kingdom
*
Address correspondence to Alicia Rosello, BSc, MPH, Modelling and Economics Unit, National Infection Service, Public Health England, 61 Colindale Ave, London, UK NW9 5EQ ([email protected]).
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Abstract

OBJECTIVES

(1) To systematically search for all dynamic mathematical models of infectious disease transmission in long-term care facilities (LTCFs); (2) to critically evaluate models of interventions against antimicrobial resistance (AMR) in this setting; and (3) to develop a checklist for hospital epidemiologists and policy makers by which to distinguish good quality models of AMR in LTCFs.

METHODS

The CINAHL, EMBASE, Global Health, MEDLINE, and Scopus databases were systematically searched for studies of dynamic mathematical models set in LTCFs. Models of interventions targeting methicillin-resistant Staphylococcus aureus in LTCFs were critically assessed. Using this analysis, we developed a checklist for good quality mathematical models of AMR in LTCFs.

RESULTS AND DISCUSSION

Overall, 18 papers described mathematical models that characterized the spread of infectious diseases in LTCFs, but no models of AMR in gram-negative bacteria in this setting were described. Future models of AMR in LTCFs require a more robust methodology (ie, formal model fitting to data and validation), greater transparency regarding model assumptions, setting-specific data, realistic and current setting-specific parameters, and inclusion of movement dynamics between LTCFs and hospitals.

CONCLUSIONS

Mathematical models of AMR in gram-negative bacteria in the LTCF setting, where these bacteria are increasingly becoming prevalent, are needed to help guide infection prevention and control. Improvements are required to develop outputs of sufficient quality to help guide interventions and policy in the future. We suggest a checklist of criteria to be used as a practical guide to determine whether a model is robust enough to test policy.

Infect Control Hosp Epidemiol 2017;38:216–225

Type
Review Article
Information
Infection Control & Hospital Epidemiology , Volume 38 , Issue 2 , February 2017 , pp. 216 - 225
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

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