Get Started with Data Disaggregation

Local data shines a light on health inequities. It shows where people and places are cut off from opportunities to be healthy, while illuminating how we can all come together to take the initiative to improve. Do you want to explore new ways to assess community health for smaller populations or geographies? Whether you are a data whiz or a community changemaker, there are opportunities to help you better understand data and move to action. Check out how three pilot project teams extended County Health Rankings & Roadmaps measures to assess populations and places within counties.

Pilots of Data Disaggregation within Counties

The County Health Rankings partnered with three groups who investigated the availability of Rankings data at geographic or demographic levels smaller than the county in their states. Their reports can provide useful information as you explore ways to access and present local data in your community:

  • California- organizations collaborated to generate a subset of the measures in the Rankings model for smaller geographies within counties (city, census tract) disaggregated by demographic groups (sex, race/ethnicity, disability status, poverty status) for California and other states to assess health and equity status.
  • Missouri- the Missouri ZIP Health Rankings Project aimed to help community health improvement stakeholders better target resources to the areas with greatest need by extending the Rankings measures to ZIP Codes.
  • New York- multiple data sources were analyzed to generate results for eleven Rankings measures for smaller geographies within counties. Data was reported for all 62 counties for use for community health needs assessments. The products included maps, tables, and graphs for easy dissemination. Supplemental resources for the NY project are included below: 

More detail on these three pilot projects are summarized in a working paper and peer reviewed article (Nguyen, et al.).

Key Lessons Learned

The teams shared key concepts and lessons learned from their pilot projects to most effectively explore and evaluate health data, the variation of health factors, and outcomes within counties. Below is an outline of their advice for conceptual development, analysis and presentation, and positioning for sustainability in disaggregating data within counties. As you begin to dig in to local data in your community, these are some things to keep in mind:

Conceptual Development – As a first step in exploring disaggregated data, take the time up front to define a target audience, select measures of interest and define geographic units and subgroups within counties. It is important to:

  • Define 2-3 priority end-users and identify why they are important.
  • Determine which potential measures most support the end users’ efforts to evaluate and improve community health.
  • Select a geographic unit and/or subgroup that are supported by available data and meaningful to end-user stakeholders.

Analysis – Not everyone has the same experience working with data and analyzing information, but there are several technical topics common to health indicator projects for small area or population sub-groups. These technical topics include generation of statistics, measure suppression, data stability, and automated production. It is helpful to:

  • Incorporate the analytical rigor needed to ensure validity and reliability while avoiding the creation of a methodologic “black-box.”
  • Develop a strategy for determining when to suppress results that strikes a balance between the need for completeness and small sample concerns about stability and/or privacy.
  • Provide descriptions of the analytic methods and limitations that are designed for audiences with different data backgrounds, such as more general Frequently Asked Questions lists and more detailed technical documentation.

Data Visualization – It is important to consider how you will design and communicate data and results to meet your end user needs. The data should be shared in an engaging and impactful way that conveys the story you are trying to tell. To do this you should:

  • Ensure that reported results are easy to understand and user-friendly for the intended audiences.
  • Consider consulting with others who can help you display your data in ways that are visually appealing (e.g. charts/maps instead of long data tables).

Positioning for Sustainability – Successfully moving with data to action means meeting the needs of end-users and planning for ways to sustain tools that are generated. To do this, it’s important to:

  • Engage key stakeholders early and often to ensure downstream value can be demonstrated.
  • Consider the full scope of resources needed to support ongoing production.
  • Develop a strategy to fund ongoing data delivery that aligns with the mission and longer-term goals.

Other Guides for Data Disaggregation

If you are looking for more guidance on data disaggregation or some next steps to evaluation, there are some other groups that lend their expertise in specific areas:

  • Annie E Casey By the Numbers- this report features the use of disaggregated data on race and ethnicity to improve the lives of children and communities, illustrating why the collection, analysis, and use of race and ethnicity data should be an integral part of any strategy, initiative or legislative agenda affecting children, families and communities.
  • Policylink: Making the Case for Data Disaggregation to Advance a Culture of Health- this report summarizes the evidence about the importance of better disaggregated data below the level of major racial groups and linking that data to the factors that influence health. It then outlines a pathway toward successful changes in critical public policies and institutional practices.
  • HEDA: Conducting a Health Equity Data Analysis- the Minnesota Department of Health provides information on how to think about and analyze data related to health equity by identifying health differences between population groups and examining their causes. This guide also serves as a starting point for understanding how to document health inequities.​

Sources of Data Disaggregation

There are some national resources that provide additional data at the city, census tract, neighborhood, or zip code level, and also among population subgroups on numerous health indicators that may be helpful in evaluating local data. Many state-specific resources also provide data below the county level for their state.

Learn about national and state-specific data sources for additional levels of geographic detail