title: Understanding the Impact of Interventions to Prevent Antimicrobial Resistant Infections in the Long-Term Care Facility: A Review and Practical Guide to Mathematical Modeling.
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.
authors.affiliation: 1Institute of Health Informatics,Farr Institute of Health Informatics Research,UCL,London,United Kingdom.
authors.forename: Alicia
authors.initials: A
authors.lastname: Rosello
authors.affiliation: 3Regional Laboratory Leeds,Public Health England,Leeds,United Kingdom.
authors.forename: Carolyne
authors.initials: C
authors.lastname: Horner
authors.affiliation: 4Healthcare Associated Infections Surveillance,National Infection Service,Public Health England,London,United Kingdom.
authors.forename: Susan
authors.initials: S
authors.lastname: Hopkins
authors.affiliation: 1Institute of Health Informatics,Farr Institute of Health Informatics Research,UCL,London,United Kingdom.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Hayward
authors.affiliation: 2Modelling and Economics Unit,National Infection Service,Public Health England,London,United Kingdom.
authors.forename: Sarah R
authors.initials: SR
authors.lastname: Deeny
mesh_terms: D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D003428:Cross Infection / Q000517:prevention & control*; D024881:Drug Resistance, Bacterial; D006090:Gram-Negative Bacteria / Q000187:drug effects*; D006801:Humans; D008134:Long-Term Care / Q000458:organization & administration; D055624:Methicillin-Resistant Staphylococcus aureus; D008962:Models, Theoretical; D009735:Nursing Homes; D017410:Practice Guidelines as Topic; D013203:Staphylococcal Infections / Q000188:drug therapy*
chemical_list: D000900:Anti-Bacterial Agents