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Emerging Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and produce early results that are consistent with the objectives of the activities and thus indicate effectiveness.
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Peer Review Study
This is a retrospective cohort study that was used to inform COVID-19 infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach. The strongest predictors of COVID-19 infection were identified as the county’s infection rate and the number of separate units in the NH; other predictors included the county’s population density, historical health deficiencies, and resident density. In addition, the NH’s historical percentage of non-Hispanic white residents was identified as a protective factor. The study concluded that a machine-learning model can help quantify and predict infection risk.
Promising Practices that show evidence of effectiveness in improving public health outcomes in a specific real-life setting, as indicated by achievement of aims consistent with the objectives of the activities, and are suitable for adaptation by other communities.
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Summary Report/Recommendations
This article explores how the relationships between vaccine site density, vaccination rates, and social vulnerability are connected across metropolitan and non-metropolitan areas in the U.S. The study uses CDC Social Vulnerability Index data combined with vaccination site density data to examine how vaccination site placement can benefit highly vulnerable populations. The results determined that while areas with higher socioeconomic vulnerability contain a large density of vaccination sites, this does not affect the low vaccination rates found in these communities. Other methods besides vaccination site placement must be considered to overcome these barriers in vaccination rates.