Introduction
Advancing equity in healthcare starts with data, but more specifically, reliable, and disaggregated data that sheds light on where disparities exist, their causes, and potential solutions.
Without it, we depend on assumptions of equitable care, ignoring the needs of a diverse patient population. And that isn’t just a missed opportunity, it’s a systemic bias. Current health data perpetuates structural inequities in its lack of detail (and disaggregation). In other words, it serves to keep disparities across social groups invisible.
So, we may have hunches based on anecdotes or observations, but, without reliable data, disparities remain hidden, and it’s hard to know what to do or whether our efforts to eliminate them are truly making a difference.
Categories to Consider:
Race, ethnicity, and language (or REaL) are the most commonly collected demographic data when looking at disparities. However, there are many categories to consider:
Employment
Race
Language
Ethnicity
Education
Income
Age
Religion
Sexual Orientation
Gender Identity
Physical/Intellectual Ability
Tribal/Indigenous Affiliation
Citizenship Status
Incarceration Status
Data to reduce disparities also includes information to further shed light on root causes of those disparities. This often involves patient surveys, interviews, focus groups, and advisory councils, as well as collaborating with other community entities who have data, information, or experiences of their own.
Key Takeaway
Achieving health equity begins with the collection and use of reliable, disaggregated data that reveals where disparities exist, why they occur, and how to address them.
Without such data, healthcare systems risk relying on assumptions that overlook the specific needs of diverse populations—thereby reinforcing systemic bias. Qualitative methods like patient interviews, surveys, and community partnerships are essential to understanding the lived experiences behind the numbers and guiding effective solutions.
