Analyzing a Community-based Coalition’s Efforts to Reduce Health Disparities and the Risk for Chronic Disease in Kansas City, Missouri
Published Date:Jun 15 2007
Source:Prev Chronic Dis. 2007; 4(3).
Although it is well known that racial and ethnic minorities in the United States have a higher prevalence of chronic diseases and a higher rate of related deaths than the overall U.S. population, less is understood about how to create conditions that will reduce these disparities.
We examined the effectiveness of a collaborative community initiative ― the Kansas City-Chronic Disease Coalition ― as a catalyst for community changes designed to reduce the risk for cardiovascular diseases and diabetes among African Americans and Hispanics in Kansas City, Missouri.
Using an empirical case study design, we documented and analyzed community changes (i.e., new or modified programs, policies, or practices) facilitated by the coalition, information that may be useful later in determining the extent to which these changes may contribute to a reduced risk for adverse health outcomes among members of the target population. We also used interviews with key partners to identify factors that may be critical to the coalition's success.
We found that the coalition facilitated 321 community changes from October 2001 through December 2004. Of these changes, 75% were designed to reduce residents' risk for both cardiovascular disease and diabetes, 56% targeted primarily African Americans, and 56% were ongoing. The most common of several strategies was to provide health-related information to or enhance the health-related skills of residents (38%).
Results suggest that the coalition's actions were responsible for numerous community changes and that certain factors such as hiring community mobilizers and providing financial support to nontraditional partners may have accelerated the rate at which these changes were made. In addition, our analysis of the distribution of changes by various parameters (e.g., by goal, target population, and duration) may be useful in predicting future population-level health improvement.
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