Mehdi Najafi1, Marek Laskowski1, Pieter T de Boer2, Evelyn Williams3, Ayman Chit4, Seyed M Moghadas1. 1. Agent-Based Modelling Laboratory, York University, Toronto, ON, Canada (MN, ML, SMM). 2. Unit of PharmacoTherapy, Epidemiology & Economics (PTEE), Department of Pharmacy, University of Groningen, Groningen, The Netherlands (PTdB). 3. Division of Long Term Care, Sunnybrook Health Science Centre, Toronto, ON, Canada (EW). 4. Sanofi Pasteur, Swiftwater, PA, USA (AC); and Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada (AC).
Abstract
BACKGROUND: Nosocomial influenza poses a serious risk among residents of long-term care facilities (LTCFs). OBJECTIVE: We sought to evaluate the effect of resident and staff movements and contact patterns on the outcomes of various intervention strategies for influenza control in an LTCF. METHODS: We collected contact frequency data in Canada's largest veterans' LTCF by enroling residents and staff into a study that tracked their movements through wireless tags and signal receivers. We analyzed and fitted the data to an agent-based simulation model of influenza infection, and performed Monte-Carlo simulations to evaluate the benefit of antiviral prophylaxis and patient isolation added to standard (baseline) infection control practice (i.e., vaccination of residents and staff, plus antiviral treatment of residents with symptomatic infection). RESULTS: We calibrated the model to attack rates of 20%, 40%, and 60% for the baseline scenario. For data-driven movements, we found that the largest reduction in attack rates (12.5% to 27%; ANOVA P < 0.001) was achieved when the baseline strategy was combined with antiviral prophylaxis for all residents for the duration of the outbreak. Isolation of residents with symptomatic infection resulted in little or no effect on the attack rates (2.3% to 4.2%; ANOVA P > 0.2) among residents. In contrast, parameterizing the model with random movements yielded different results, suggesting that the highest benefit was achieved through patient isolation (69.6% to 79.6%; ANOVA P < 0.001) while the additional benefit of prophylaxis was negligible in reducing the cumulative number of infections. CONCLUSIONS: Our study revealed a highly structured contact and movement patterns within the LTCF. Accounting for this structure-instead of assuming randomness-in decision analytic methods can result in substantially different predictions.
BACKGROUND: Nosocomial influenza poses a serious risk among residents of long-term care facilities (LTCFs). OBJECTIVE: We sought to evaluate the effect of resident and staff movements and contact patterns on the outcomes of various intervention strategies for influenza control in an LTCF. METHODS: We collected contact frequency data in Canada's largest veterans' LTCF by enroling residents and staff into a study that tracked their movements through wireless tags and signal receivers. We analyzed and fitted the data to an agent-based simulation model of influenza infection, and performed Monte-Carlo simulations to evaluate the benefit of antiviral prophylaxis and patient isolation added to standard (baseline) infection control practice (i.e., vaccination of residents and staff, plus antiviral treatment of residents with symptomatic infection). RESULTS: We calibrated the model to attack rates of 20%, 40%, and 60% for the baseline scenario. For data-driven movements, we found that the largest reduction in attack rates (12.5% to 27%; ANOVA P < 0.001) was achieved when the baseline strategy was combined with antiviral prophylaxis for all residents for the duration of the outbreak. Isolation of residents with symptomatic infection resulted in little or no effect on the attack rates (2.3% to 4.2%; ANOVA P > 0.2) among residents. In contrast, parameterizing the model with random movements yielded different results, suggesting that the highest benefit was achieved through patient isolation (69.6% to 79.6%; ANOVA P < 0.001) while the additional benefit of prophylaxis was negligible in reducing the cumulative number of infections. CONCLUSIONS: Our study revealed a highly structured contact and movement patterns within the LTCF. Accounting for this structure-instead of assuming randomness-in decision analytic methods can result in substantially different predictions.
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