Who gets counted in data shapes whose needs are seen, funded, and addressed. When you see data driving big decisions, check who is included and who is missing. Otherwise, entire communities can be overlooked.
Bottom Line Up Front (BLUF): When people are counted accurately, their needs become visible. This visibility is the first step toward action. Better data helps ensure that decisions, funding, and care reflect the communities they are meant to serve.
Data shapes what we see and what we miss. From how public money is spent to which communities receive health services, data plays a major role in everyday decisions. Yet many data systems were built on federal race and ethnicity categories that have changed little since the 1970s, failing to reflect the diversity of today’s population. When people are counted incorrectly or not counted at all, they do not just disappear from data. They become invisible in the policies and programs meant to support them.
Who is left out and why
Certain communities are left out of data not because they are small, but because the systems designed to count them were never built with them in mind. Middle Eastern and North African (MENA) populations, for example, have long been classified as “white” in federal datasets, masking differences in health, education, and access to services. In Nevada, where Arab communities are among the fastest growing in the country, this misclassification has made many MENA residents invisible in state data until a new law allowed people to identify under a distinct MENA category.
Urban American Indian and Alaska Native (AI/AN) communities face similar challenges. Even though most AI/AN people live in cities, they are often counted incorrectly, grouped into broad categories like “other”, or left out when the number of people is small. These choices are often made to protect privacy. But when data tools do not reflect Native communities or clearly explain how decisions are made, important differences in health and access are hidden.
Why this matters
When people are missing or misclassified in data, their needs are easier to ignore. Health problems can look smaller than they really are, which means fewer resources, less funding, and weaker services for the communities most affected.
This can also delay action during emergencies. For example, during the COVID-19 pandemic, Santa Clara County began breaking down case data for different Asian ethnic groups. This showed that Filipino and Vietnamese residents were getting sick at higher rates than other Asian American groups. Once this was clear, officials were able to focus outreach and education in those communities.
Data gaps also affect political power. Communities that are not clearly counted may receive less representation and fewer protections. And when small numbers are dismissed as “not statistically significant” due to limited data or inadequate study design, real harm may be overlooked. Lack of data does not mean lack of need. It often means people were never fully counted.
How to make sure everyone is counted
Federal guidelines should be treated as a starting point, not a limit. While they set a basic standard for data collection, state and local agencies, public health organizations, and researchers can and should go further, with the ability to aggregate data back to federal standards as needed. The choices they make about how data is collected and shared determine who is seen and whose needs are addressed.
To count people more accurately, data systems can improve in several ways:
- Collect more detailed demographic data, including race, ethnicity, gender, language, & disability so people can describe themselves accurately, not just fit into broad categories. If offering many categories is not feasible, include a free-text “Other” option, with the understanding that this requires additional qualitative analysis by analysts.
- Treat federal guidelines as a starting point, and allow states and local agencies to collect more specific data when needed.
- Use mixed methods, combining numbers with surveys and interviews to better capture lived experiences.
- Partner with communities to design questions that are respectful and culturally meaningful.
- Be transparent about data limits, clearly explaining when data is missing, uncertain, or based on small sample sizes.
- Rethink data suppression, using longer time frames or grouped data instead of hiding small but important numbers.
