Equity Data Navigator
Explore Health Equity in Your Community
The Equity Data Navigator shows the relationship between race, the social determinants of health, and health outcomes. Here are a few of our findings:
In the Navigator, we measure health outcomes by looking at a person's and their status. Some bordering neighborhoods within just a few blocks of each other see a difference in life expectancy of more than 10 years.
Across Colorado, factors such as higher educational attainment and having health insurance are associated with a better self-reported health status.
The model also showed that in areas with richer racial/ethnic diversity, systemic issues—such as a lack of affordable homes, poor walkability around neighborhoods, and limited access to food—caused lower scores in the SDOH index, ultimately yielding poorer health outcomes.
What Are the Social Determinants of Health (SDOH)?
Explore Health Equity in Your Community
Resources
Our resource library of tools, data sources, reports, and more can help you learn more about health equity issues and advocate for community change.
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Data Transparency
February 2024
The Equity Data Navigator is one of the tools available to communities on the Colorado Equity Compass (CEC) website. The Equity Data Navigator allows users to understand how their area is doing compared to the state and other communities on a variety of health outcomes and social factors that contribute to a person's health. The tool displays data from a variety of data sources. A few of the measures in the Equity Data Navigator use data from administrative sources, such as state voter registration counts or school district data on average reading scores. The source of many other measures in the Equity Data Navigator is survey data from the U.S. Census Bureau's American Community Survey (ACS).
Download the data with information about methodology here.
Limits in the data.
Despite its wide use, the ACS and other surveys have limitations that are important to point out. Because a survey reflects only a random subset of the population—a representative sample—the numbers from the survey are considered estimates, and each of these estimates have a margin of error. Estimates that reflect relatively small groups of people or small geographic regions (like census tracts) may show volatility—or jump around—from year to year because of the small sample size. To manage this volatility, the CEC uses estimates based on five years of combined ACS data covering 2018-2022. While this approach improves the stability of the estimate, it comes with its own challenges. Combining years of data makes comparison over time challenging, because the Census Bureau recommends only comparing 5-year estimates that do not overlap. For example, one can compare a 2013-2017 estimate to a 2018-2021 estimate but should not compare a 2016-2020 estimate to a 2018-2021 estimate. Because of this, users should not compare current CEC data to data previously published on the website. The Census Bureau redraws census tract boundaries every decade based on population changes. Data was not available at the 2020 census tract level for a small number of metrics. In these cases, calculations were used to arrive at small area estimates for data only available at the county/regional level and “crosswalk” estimates for data only available for 2010 census tract boundaries. Some areas in the Equity Data Navigator may indicate that the proportion of a county or census tract’s population represented by a particular demographic group is 0.0%. Due to the margin of error, an estimate of zero does not necessarily mean that there are no people of that particular group living in that area. Any estimates of zero are not intended to diminish the importance of any group of people living in that area; rather, these figures simply represent the best estimate given the available data. In addition, we know from the U.S. Census Bureau's own research that some populations may be undercounted in its Census data. We anticipate that some populations—like people experiencing homelessness or those who are undocumented—may be overlooked or underrepresented in survey data as well because of the methods used to collect the data or because of hesitancy and mistrust in completing the survey.