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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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Case Study
This article describes testing efforts in Georgia’s long-term care facilities and the impact that preventative routine screening had on COVID-19 cases among residents/staff. Facilities that had regular preventative testing rather than testing only after learning of a confirmed case had a lower initial prevalence of COVID-19 and fewer subsequent cases.
Best Practices that show evidence of effectiveness in improving public health outcomes when implemented in multiple real-life settings, as indicated by achievement of aims consistent with the objectives of the activities.
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Peer Review Study
This resources describes the creation of a culturally appropriate oral health/healthy living workshop curriculum for the Sikh American community. Community educators were Punjabi-speaking and members of the Gurdwaras ethic group, to whom the interventions were delivered. Statistically significant improvements in health behaviors were self-reported after attending the program.
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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Summary Report/Recommendations
This practice describes a Three-Phase Approach to mitigating COVID-19 in long-term and post-acute care nursing facilities in the Seattle, WA area. The authors outline a structure for addressing the pandemic based on disease surveillance measures, with different focus areas within each phase. Measures include:
(1) Initial: Communication, tracking, PPE preparation
(2) Delayed: Education, testing, isolation
(3) Surge: Activation of a “drop team”” of health care professionals during an outbreak
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 case study is on the effectiveness of COVID-19 vaccine distribution via mobile vans to residents/staff of 47,907 long-term care facilities (LTCFs) across the United States that relied on algorithms to optimize vaccine distribution. The authors developed a modeling framework for vaccine distribution to high-risk populations in a supply-constrained environment. The framework decomposed this challenge as two separate problems: an assignment problem, where they optimally mapped each LTCF to select CVS stores responsible for vaccines; and a scheduling problem, where they developed an algorithm to assign available resources efficiently. The learning and this framework may be of use to other organizations, including communities where mobile clinics can be established to efficiently distribute vaccines and other healthcare resources in a variety of scenarios.
Best Practices that show evidence of effectiveness in improving public health outcomes when implemented in multiple real-life settings, as indicated by achievement of aims consistent with the objectives of the activities.
RELEASE DATE:
Peer Review Study
This article describes the approach and impact of the Stanford Flu Crew, a service learning program at Stanford University School of Medicine, where pre-clinical students provide vaccines to underserved populations in community settings. The article includes information on both program outcomes (i.e., the number of people vaccinated per year over a 4-year period) and student perceptions of learning outcomes achieved through this program.
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.