Achieving health equity requires building communities’ capacity to mobilize, organize, and strategize to change the conditions—or social determinants of health—that impact their well-being. Community and resident-driven organizations need data to 1) bring attention to the disparities and move people into action, 2) understand the root causes and organize across sectors to deal with those root causes, 3) inform solutions that will create lasting change, and 4) monitor the change to continuously adapt and improve the solutions. To achieve all these, communities have to be able to access, share, and transform data into actionable knowledge.
Section 4302 of the Affordable Care Act and the federal government’s increased willingness and capacity to make data available to the public have elevated the importance of data in generating solutions to end health inequities. Today’s technology has stimulated the expansion of data platforms that allow users to download data and use data visualization techniques to illustrate health, education, housing, and other disparities by race and ethnicity, gender, geography, and other variables. Many guides have been published to assist communities to access and use data. In addition, there are many national organizations and groups such as the Community Indicators Consortium, National Neighborhood Indicators Project, and Healthy Communities Institute that are actively working with local data collaboratives to facilitate a learning community of people working at the nexus of data analytics and community change. There are even more local organizations with a similar agenda, such as The Piton Foundation, DataHaven, and Data Driven Detroit that are serving as intermediaries for data gathering, analysis, and use.
Despite these advances and resources, many community and resident-driven organizations struggle with how to effectively use data to design or adapt the appropriate change strategies for their communities. Their difficulties can be traced to at least one of the following causes. These challenges were confirmed in a national environmental scan of 157 community-based organizations to assess their use of data, conducted by Community Science in 2012:
The above challenges are well documented, albeit to varying degrees, in the scientific and practice literature. There are two additional challenges that Community Science frequently encounters during our interactions with community and resident-driven organizations that participate in collaboratives aimed at generating data-informed solutions and yet receive insufficient attention from funders, intermediaries, and other supports.
First, some people would consider data “sexy.” The appeal of data can easily distract from the urgency of solutions to improve the social determinants of health that are negatively affecting a community. Data can be sliced and diced in multiple ways. It is easy for researchers or anyone else involved in data acquisition and analysis to become immersed in the data. They have the skills and tools to generate spectacular presentations of data to illustrate the conditions of a community, from dashboards and scorecards to interactive maps and graphs. If necessary, they have the ability to go back to the data and dig deeper.
What often gets lost in the above process is the answer to the question, “So what?” If an interactive map shows a high infant mortality rate in a particular section of a city, so what? What is happening in that area? Who is affected? What is contributing to the high infant mortality rate? How does the trend impact the health and well-being of everyone living in the city, not just the residents in that particular section? What solutions are needed? Who or which institutions are responsible for implementing the solutions? Herein lies the second challenge.
In the attempt to answer the above questions, community and resident-driven organizations—that have firsthand knowledge about the conditions in their community—must be engaged in the process in a substantial or meaningful way. They bring valuable insights into what they believe are the everyday conditions that contribute to the disparate health outcomes in their community. They can explain what the data mean, why the measures of the conditions change, and what their underlying causes are; in short, they can connect the dots. There are many ways to ensure their participation, including involving them from the outset of the effort in determining the rules of engagement, describing the problem, identifying existing strengths that can be built on to mobilize their community into action, generating the solutions that are sustainable and most appropriate for their community, and sharing any additional resources that the collaborative will leverage in the future to implement the solutions.
At the heart of the above process is transformation of conflict and sharing of power. Organizations that are small and with limited capacity and access to resources, but nevertheless are knowledgeable about the communities in which they work are not always treated as equals by larger established institutions. Also, they usually have different priorities. Collaboratives must have access to expertise to deal with conflict and change the status quo in relationships. Funders should not assume that people with similar goals and shared values will naturally and effectively collaborate when brought together. They should expect the process to take time and produce tensions. Sometimes, the pressures may be directed at them because they are part of the system that perpetuates the inequities.
Here are some of the lessons learned and challenges to date in building community capacity to access, share, and transform data into actionable knowledge.
In summary, building community capacity to access, share, and transform data into actionable knowledge is not as easy as it looks. Thankfully, there is a rich literature around community collaboration and community engagement to draw from and expansion of stories about how communities use data to inform change strategies. What is missing is how all this knowledge can be combined to form a framework and guiding principles for how organizations can work effectively at the nexus of data analytics, collaboration, and community engagement. Only then will innovative solutions be generated to achieve health equity. Community Science is helping to fill this gap through a study to develop and test such a framework, specifically with regard to administrative data.
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