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 study analyzed 30-day outcomes of COVID-19 patients surviving to discharge across a five-hospital health system.
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 examined the impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread by fitting compartmental SEIR (Susceptible-Exposed-Infectious-Removed) transmission models structured by race and ethnicity to seroprevalence data from New York City and Long Island and analyzing how herd immunity thresholds, final sizes, and epidemic risk change across groups. The results highlight the importance of developing socially informed COVID-19 transmission models that incorporate patterns of epidemic spread across racial and ethnic groups.
Novel Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and are in the process of generating evidence of effectiveness or may not yet be tested.
RELEASE DATE:
Peer Review Study
This study used machine learning to analyze electronic health records from an urban academic medical center and to investigate whether providers’ use of negative patient descriptors varied by patient race or ethnicity.
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
The purpose of this study was to provide healthcare decision-makers in North Carolina with information about the available health workforce in order to conduct workforce surge planning and to anticipate concerns about professional or geographic workforce shortages. Descriptive and cartographic analyses were conducted using licensure data to assess the supply of respiratory therapists, nurses, and critical care physicians. Licensure data were merged with population data and numbers of intensive care unit beds. Higher concentrations of healthcare workers were observed in urban areas. Critical care physicians were primarily based in areas with academic health centers.
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
Using jail population data, county-level aggregate data, and policy intervention data, this study examines the association of jail decarceration and anticontagion policies with COVID-19 rates. This study adds a unique contribution to the discussion of incarceration and disease spreading, as it is the first to examine the effects of decarceration on population-level community health outcomes. The authors compare anticontagion policies within jails throughout the United States to other community policy interventions such as stay-at-home orders, nursing home visitation bans, school closures, and mask mandates. Additionally, the study analyzes four demographic subsets including income, population density, and median proportion of populations identifying as Black. The results showed that an 80% reduction in jail incarcerations would decrease disease spreading in both the prison system and the community, and was more effective than any other community policy intervention.
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
Using patient record data from the New York University Langone Health System, this study examines outcomes from individuals who tested positive for COVID-19 in New York City from March 1 through April 8, 2020, to examine differences in outcomes based on age, sex, body mass index, comorbidity, insurance type, and neighborhood socioeconomic status. The results indicated that while Black and Hispanic communities were experiencing larger mortality rates in the general population, the study did not find that Black and Hispanic individuals are experiencing worse COVID-19 outcomes, including mortality when hospitalized, as compared to hospitalized patients who are white. This study supports the idea that existing social determinants of health, such as access to housing, access to health care, differential employment outcomes, and poverty can impact mortality rates for Black and Hispanic communities.
Novel Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and are in the process of generating evidence of effectiveness or may not yet be tested.
RELEASE DATE:
Peer Review Study
This study adapted the Lorenz curve methodology to characterize disparate outcomes in COVID-19 testing across time, regions, and ZIP codes for the St. Louis and Kansas City regions. The results showed that Black individuals have half the rate of testing per case than White populations, even among Black and White individuals residing in the same ZIP code. The study calls for equitable testing strategies and routine monitoring using formal metrics to inform adaptive testing strategies.
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 used publicly available data on COVID-19 cases and deaths in the United States counties to conduct an analysis that described racial disparities in COVID-19 disease and death and associated determinants. The results of the study showed that counties with higher proportions of black people had higher prevalence of COVID-19 cases and deaths. This study also recommends that county level comparisons can inform COVID-19 responses and identify hot spots to show that social conditions, structural racism, and other factors increase the risk for COVID-19 diagnosis and deaths in Black communities.
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 study used Centers for Disease Control and Prevention Social Vulnerability Index (SVI) data combined with COVID-19 vaccine administrative data to conduct a county-level analysis of vaccination coverage across states (defined as individuals receiving at least one dose of a COVID-19 vaccine between December 14, 2020, and March 1, 2021). The results demonstrated that vaccination coverage was lower in higher vulnerability states, with coverage significantly lower for individuals with socioeconomic vulnerabilities (such as level of education). The study details vaccination coverage for 49 states and Washington, D.C. (excluding Hawaii). Further research should be conducted on local vaccination status to further elucidate areas of high vulnerability to achieve COVID-19 vaccination equity.
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.