Background
Methods
Results
Role of health-care workers (HCWs) and residents in transmission mechanisms within NHs
Intervention strategies to control MRSA transmission within NHs
Persistence of MRSA within NHs
Outbreak potential within CFs
Impact of CF-community MRSA dynamics
Impact of LTCF-hospital MRSA dynamics
Intervention strategies to control inter-facility MRSA transmission
Modelling frameworks
Settings | Nursing Homes | ||
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Articles | Chamchod et al. (2012) [22] | Batina et al. (2016a) [23] | Batina et al. (2016b) [24] |
Aims | 1. Study MRSA dissemination 2. Study persistence and prevalence of MRSA 3. Study intervention controls | 1. Assess MRSA epidemic potential 2. Determine conditions at which USA300 and non-USA300 could be eliminated or reduced 3. Evaluate the impact of recent antibiotics exposure on MRSA prevalence and Ro | 1. Predict long-term prevalence of USA300 and non-USA300 2. Assess the influence of potential risk factors on MRSA acquisition rates and average duration of colonization |
Country (model inference) | Non-specific a | Wisconsin, United States | Wisconsin, United States |
Model | |||
Typeb | Compartmental (deterministic); Markov process (stochastic) | Compartmental (deterministic) Markov process (stochastic) | Markov chain model |
Forecast period | 1200/2000/4000 days | 20 years to 30 years | 120 months |
Disease progression | |||
Host | Residents | Residents | Residents |
Vector | HCWs | Not applicable | Not applicable |
States involved among hosts | Susceptible, Colonized | Susceptible, Colonized | Susceptible, Colonized |
States involved among vectors | Decontaminated, contaminated | Not applicable | Not applicable |
MRSA Strains involved | MRSA as a whole | USA300, non-USA300 | USA300, non-USA300 |
Stratified by hosts’ recent antibiotics exposure | No | Yes | Yes |
Transmission pathways | |||
Endogenous | |||
Residents to Residents | Yes | Yes | Not applicable d |
Residents to HCWs | Yes c | No | Not applicable d |
HCWs to Residents | Yes c | No | Not applicable d |
HCWs to HCWs | No c | No | Not applicable d |
Exogenous | |||
Importation of colonized cases | Yes | Yes | Not applicable d |
Settings | Correctional facilities | ||
Articles | Hartley et al. (2006) [27] | Kajita et al. (2007) [25] | Beauparlant et al. (2016) [26] g |
Aims | 1. Calculate the epidemiological weighte of an institution / subpopulation | 1. Assess outbreak severity 2. Determine the conditions and consequences of outbreaks 3. Design interventions to control outbreaks | 1. Determine effect of community dynamics on MRSA dynamics in prisons 2. Determine the effect of recidivisms on disease dynamics |
Country (model inference) | Non-specific f | Los Angeles, United States | United States |
Model | |||
Typeb | Mathematical formula | Compartmental (deterministic, stochastic) | Compartmental (deterministic) |
Forecast period | Not applicable | 9 months | 1000 days |
Disease progression | |||
Host | Inmates | Inmates | Community, Inmates, Recidivists |
States involved among hosts | Colonized, Non-colonized | Susceptible, Colonized, Infected | Susceptible, Infected |
Strains involved | MRSA as a whole | CA-MRSA | MRSA as a whole |
Stratified by hosts’ recent antibiotics exposure | No | No | No |
Transmission pathways | |||
Endogenous | |||
Inmates to Inmates | Not applicable | Yes h | Yes h,i |
Inmates to Staff | Not applicable | No | No |
Staff to Inmates | Not applicable | No | No |
Exogenous | |||
Importation of colonized cases | Not applicable | Yes | Yesj |
Settings | Inter-facilities | ||
Articles | Barnes et al. (2011) [28] | Lesosky et al. (2011) [31] | Lee et al. (2013a) [29] Lee et al. (2013b) [30] |
Aims | 1. Predict long-term prevalence of facilities 2. Assess the effects of facility size, patient turnover and decolonization on MRSA prevalence | 1. Determine how patient transfers affect MRSA transmission among patients in hospitals and NHs | [29]: 1. Quantify how MRSA prevalence in NHs affect those in hospitals [30]: 1. Compare different contact intervention strategies (no intervention VS only clinically apparent MRSA infections VS all MRSA carriers) |
Country (model inference) | Non-specific f | Non-specific k | California, United States |
Model | |||
Typeb | Hybrid simulation model l | Stochastic, discrete time Monte Carlo simulation model | Agent-based model |
Forecast period | Not explicitly stated | 365 days | [29]: 5 years after outbreak [30]: 5 years after outbreak implementing contact precautions |
Facility involved | Hospitals, General LTCFs | Teaching hospitals (THs)m, Non-teaching hospitals (NTHs)m, NHs | Hospitals, NHs |
Agent unit | Facility | Individual | Individual |
Disease progression | |||
States involved | Susceptible, Persistently colonized, Colonized | Susceptible, Colonized/Infected | Susceptible, Colonized |
Strains involved | MRSA as a whole | MRSA as a whole | MRSA as a whole |
Transmission pathways | |||
Intra-facility | |||
Hospitals | |||
Patients to patients | Yes | Yes | Yes |
Patients to HCWs | No | No | No |
HCWs to HCWs | No | No | No |
HCWs to patients | No | No | No |
NHs/LTCFs | |||
Residents to residents | Yes | Yes | Yes |
Residents to HCWs | No | No | No |
HCWs to HCWs | No | No | No |
HCWs to residents | No | No | No |
Inter- facility (patient sharing) | |||
Hospitals to Hospitals | No | Yes | Yes |
LTCFs/NHs to LTCFs/NHs | No | No | Yes |
Hospitals to LTCFs/NHs | Yes | Yes | Yes |
LTCFs/NHs to Hospitals | Yes | Yesn | Yesn |