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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.
RELEASE DATE:
Peer Review Study
This study develops a microsimulation model of COVID-19 transmission in a homeless shelter and calibrated it to data from surveys conducted during COVID-19 outbreaks in five homeless shelters in three US cities from March 28 to April 10, 2022. The study estimates the probability of averting a COVID-19 outbreak when an exposed individual is introduced into a representative homeless shelter of 250 residents and 50 staff over 30 days under different infection control strategies. The results show that within communities with high COVID-19 community incidence are unable to prevent a large outbreak, despite extensive infection control strategies. This study suggests a need for non-congregate housing in high-risk settings, is needed to avoid outbreaks within these settings.
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 study looks at a COVID-19 outbreak among a multi-residential home for adults with intellectual and developmental disabilities in Arizona to determine how COVID-19 was spread. Epidemiologic and genomic evidence show that spread likely occurred from asymptomatically infected staff. This study demonstrates the need for public health measures and rapid genomic analysis to shape policies that protect these vulnerable 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 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.
RELEASE DATE:
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.