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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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Other
This episode from the podcast 99 Percent Invisible reflects on how the COVID-19 pandemic exposed the American public to a long-standing problem: the disjointed nature of the U.S. health system. Because state and local health departments largely operate independently and use their own data collection and analysis tools, health systems across the country lack standardized data definitions and systems. These inconsistencies made it nearly impossible to collect and analyze comprehensive, standardized data on COVID-19 cases, deaths, and vaccines administered amid the pandemic. Health experts featured on this episode believe that the pandemic made the need for an overhaul of America’s informatics system very apparent. When rebuilding this system, it’s important to focus on remedying existing inequalities in data collection and classification that in some cases render the health status of certain populations – think Native American communities and other communities of color – completely invisible in the data. By virtue of being small populations, it can be difficult for health departments to collect sufficient and/or statistically significant data on minority communities. Another issue discussed in this episode is the use of broad racial categories like “other,” “multiple races,” or even “Asian American,” which, if not disaggregated, obscures the health status of diverse populations who are grouped under the same category. Without comprehensive and inclusive health data, it’s difficult to identify disparities and implement policies and programming that promote social mobility and health equity.
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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Toolkit
The Health Equity Assessment Toolkit (HEAT) is a software application that facilitates the assessment of within-country health inequalities. It was developed for use on desktop or laptop computers and mobile devices. Explore inequality, which enables users to explore the situation in one setting of interest (e.g. a country, province or district) to determine the latest situation of inequality and the change in inequalities over time. Compare inequality, which enables users to benchmark, i.e. compare the situation in one setting of interest with the situation in other settings.
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.
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Toolkit
The Community Information Exchange (CIE) Data Equity Framework’s goal is to build data systems to help institutions, and the communities they serve, approach CIE® planning and systems change work from a place of anti-racism by: (1) naming how data system design reflects understanding of and participation by the intended beneficiaries of current programs and interventions; (2) acknowledging and documenting the effects of a spectrum of data system design types on oppressed populations and communities; (3) identifying strategies needed to eliminate the harm of current processes and practices; (4) highlighting the behavior change needed to rebuild or change the overall data system to better meet community needs across racial and ethnic populations; and (5) adopting practices that promote restorative justice and mitigate harm and exploitation.