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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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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.
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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Data Collection Tool
AAPI Data is a publisher of demographic data and policy research on Asian Americans and Pacific Islanders.
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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Data Collection Tool
The National Equity Atlas is a first-of-its-kind web resource for data to track, measure, and make the case for inclusive growth. The Atlas provides deeply disaggregated, longitudinal data on demographic change, racial and economic inclusion, and the economic benefits of equity for the largest 100 cities, largest 150 regions, all 50 states, and the United States. The data can be used for data collection and analysis.
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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Data Collection Tool
This dashboard provides national-level demographic COVID-19 vaccination data for data collection and analysis. The data is available by race, sex, and age.
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:
Data Collection Tool
The Mapping Medicare Disparities (MMD) Population View provides a user-friendly way to explore and better understand disparities in chronic diseases, and allows users to: (1) visualize health outcome measures at a national, state, or county level; (2) explore health outcome measures by age, sex, race and ethnicity; (3) compare differences between two geographic locations (e.g., benchmark against the national average); and (4) compare differences between two racial and ethnic groups within the same geographic area.