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Understanding Place-Based Health Inequalities in Mid-Life (R01 Clinical Trial Not Allowed)

Sector: Government • Location: United States of America

Source: Grants.gov

Project
Archived

This Funding Opportunity Announcement (FOA) RFA supports secondary data analyses and/or data collection/enhancements to existing datasets to address the role of place (e.g., countries, U.S. Census regions, states, counties, neighborhoods, and locations across the urban-rural continuum) in health in order to uncover actionable knowledge to address disparities by geography and other factors such as

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The project “Understanding Place-Based Health Inequalities in Mid-Life (R01 Clinical Trial Not Allowed)” is an infrastructure initiative in the Government sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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archived

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Description

Description

This Funding Opportunity Announcement (FOA) RFA supports secondary data analyses and/or data collection/enhancements to existing datasets to address the role of place (e.g., countries, U.S. Census regions, states, counties, neighborhoods, and locations across the urban-rural continuum) in health in order to uncover actionable knowledge to address disparities by geography and other factors such as race and ethnicity. Secondary data analyses appropriate to this FOA include those that: 1) clarify social, economic, behavioral, and ?institutional (e.g., federal to local government policies/programs, firm/industry practices, etc.) explanations for place-based health disparities (levels and trends) and/or 2) examine intersections between place and sociodemographic characteristics (e.g., gender, race, ethnicity, etc.) to better understand and address processes driving other ?health disparities. Analytic approaches that utilize quasi-experimental and other methods that yield causal estimates are preferred, though mixed methods projects that inform mechanistic insights and/or data enhancements are also appropriate. Multilevel analyses that enable the joint and synergistic examination of macro-, meso-, and individual-level factors are also encouraged.

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Source reliability

High

Data quality score

100%

Source

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URL

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