Modeling the impact of health care worker masking to reduce nosocomial SARS-CoV-2 transmission under varying adherence, prevalence, and transmission settings.

Abstract

Objectives: To understand the scenarios where health care worker (HCW) masking is most impactful for preventing nosocomial transmission.

Methods: A mathematical agent-based model of nosocomial spread with masking interventions. Masking adherence, community prevalence, disease transmissibility, masking effectiveness, and proportion of breakroom (unmasked) interactions were varied. The main outcome measure is the total number of nosocomial infections in patients and HCW populations over a simulated three-month period.

Results: HCW masking around patients and universal HCW masking reduces median patient nosocomial infections by 15% and 18%, respectively. HCW-HCW interactions are the dominant source of HCW infections and universal HCW masking reduces HCW nosocomial infections by 55%. Increasing adherence shows a roughly linear reduction in infections. Even in scenarios where a high proportion of interactions are unmasked "breakroom" interactions, masking is still an effective tool assuming adherence is high outside of these areas. The optimal scenarios where masking is most impactful are those where community prevalence is at a medium level (around 2%) and transmissibility is high.

Conclusions: Masking by HCWs is an effective way to reduce nosocomial transmission at all levels of mask effectiveness and adherence. Increases in adherence to a masking policy can provide a small but important impact. Universal HCW masking policies are most impactful should policymakers wish to target HCW infections. The more transmissible a variant in circulation is, the more impactful HCW masking is for reducing infections. Policymakers should consider implementing masking at the point when community prevalence is optimum for maximum impact.