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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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Case Study, Key Informant Interview
This case study discusses how Hawaii’s Behavioral Health Administration (BHA) partnered with state housing services to organize isolation and quarantine services for people experiencing homelessness. This department also focused on substance use disorder and mental illness, so they worked to build partnerships with treatment centers that individuals could enter after quarantine. The BHA has also focused on braiding funding sources between substance use disorder and homelessness efforts to provide more wraparound services and combat the siloization of different departments.
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 special edition data tool provides important information related to the COVID-19 pandemic, such as data regarding where populations vulnerable to the COVID-19 pandemic reside, where the cases are surging, and which communities will require greater hospital capacity for severe COVID-19. 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.
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.