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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:
Case Study, Peer Review Study
This was a randomized, quasi-experimental study of an intervention that was implemented among rural and urban populations to encourage online grocery shopping and more nutritious food purchases. The authors targeted rural counties with high poverty rates for recruitment. The results showed that online shopping can improve food shopping habits and accessibility.
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, Systematic Review/Meta-Analysis
The authors conducted a systematic review to understand disparities in H1N1 vaccine uptake by race/ethnicity, socioeconomic status, rural/urban residence, population density, and disability status, and factors associated with unequal uptake, as well as the benefits and harms of interventions designed to attenuate inequities in H1N1 vaccine uptake—in an effort to address potential disparities in COVID-19 vaccine access and uptake.
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 cross-sectional study uses Lorenz curves as a metric for quantifying racial inequities in COVID-19 testing.
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 examined internet usage of older adults through the California Health Interview Survey to determine how social determinants of health and socioeconomic levels can impact access to health information. The results showed that minorities with lower levels of socioeconomic status were most impacted by a digital divide and access to health information via the internet.
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 assessed community factors associated with COVID-19 infection levels and testing rates at the US Census tract level in Seattle, King County, Washington. Through multivariate models, the study demonstrated disparities for communities of color within the county, for risk of exposure, transmission, and in testing rates. The results show a need for increased education, training, and disease control resources for communities with low socioeconomic status and 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 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.