Using simulation modeling to inform intervention and implementation selection in a rapid stakeholder-engaged hybrid...

Area of Science:

• Public Health
• Epidemiology
• Health Services Research

Background:

• Implementation research typically assumes pre-established evidence-based practices, which is often unfeasible during public health emergencies.
• This study addresses the need for rapid intervention design in crisis situations, focusing on vulnerable populations in group homes.
• Utilizes a simulation model to inform a stakeholder-engaged process for developing tailored COVID-19 mitigation strategies.

Purpose of the Study:

• To employ a simulation model to guide the rapid development of a tailored intervention and implementation strategy for COVID-19 mitigation.
• To inform a stakeholder-engaged process for protecting individuals with serious mental illness (SMI) and intellectual/developmental disabilities (ID/DD) in group homes.
• To assess the effectiveness of various COVID-19 mitigation strategies in a group home setting.

Main Methods:

• A validated dynamic microsimulation model of COVID-19 transmission was used to evaluate mitigation strategies for group home staff and residents.
• Model inputs were derived from stakeholder data, public records, and literature, assessing strategies like screening, testing, isolation, PPE, and vaccination.
• Multidisciplinary stakeholders provided input iteratively as the pandemic evolved, with sensitivity analyses conducted for parameter uncertainty.

Main Results:

• Vaccination was projected to yield the largest decrease in infections; increasing uptake to 90% significantly reduced resident and staff infections.
• Routine testing showed minimal benefit, while simulated removal of masking increased infections.
• Simulation results informed the selection of a "Tailored Best Practice" intervention package for a hybrid trial.

Conclusions:

• Vaccination and addressing vaccine hesitancy among staff are crucial for mitigating COVID-19 risk in group homes.
• Simulation modeling proved effective for rapidly informing prevention and implementation strategy selection in a hybrid trial.
• This simulation-based approach is valuable for future rapid deployment of interventions when tailored data is scarce.