Find Resources
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 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.
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 describes partnership and community capacity building efforts and examines community impact, defined as successful linkages to resources and changes in stress tolerance capacity among community members. Community capacity building was conceptualized as dissemination of trauma-informed education and training, community outreach and engagement, and linkage of community members to resources. Findings include: training opportunities were widespread, resource linkage type shifted from basic services and health care to food distribution, and significant improvements occurred in coping through emotional and instrumental support (did not report stress tolerance). This demonstrated the effectiveness of community-based partnerships as capacity building strategies, as partnerships had already laid the groundwork and established trust within their communities, resulting in a nimble, local response to a global crisis. The partnerships’ response to the pandemic shows how organizations that are part of a network are able to leverage resources, new ideas, and knowledge to respond to community needs.
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:
Summary Report/Recommendations
This article describes policy, regulatory, and payment reforms implemented in Massachusetts in response to people with serious mental illnesses that are at disproportionate risk of COVID-19 morbidity and mortality. The reforms include: (1) ensuring continuity of care for individuals with serious mental illness during the COVID-19 pandemic and (2) supporting community-based behavioral health organizations.
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.