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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 article discusses the use of wastewater surveillance to indicate new levels of COVID-19 or other infection in congregate housing settings. The study sampled wastewater from a hospital and a wastewater treatment plant to detect levels of COVID-19 in the individuals residing in the hospital. The results were able to indicate levels of COVID-19 in the wastewater, but were unable to distinguish between new infection levels and residual viral shed from previously infected patients. This study shows the potential of wastewater management, and calls for the increased refinement of the process to more accurately monitor viral spread in vulnerable living situations.
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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Peer Review Study
This article conducted a cross-sectional study of 351 Massachusetts cities and towns from January 1-May 6, 2020, to understand what demographic, economic, and occupational factors are affecting COVID-19 incidence rates. Results found that non-Latino Black and Latino populations are at most risk of contracting COVID-19. Addressing factors like healthcare access for foreign-born non-citizens, crowded housing, and the protection of food service workers may help mitigate spread among minority populations.
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.
RELEASE DATE:
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.