Why the CDC Collects Data on Different Groups: Key Reasons & Impact

Table of Contents#

1. Understanding the CDC's Mission: Data as the Foundation
2. The Importance of Disaggregated Data: Beyond "Average" Health
3. Key Reasons the CDC Collects Data on Different Groups
   • 3.1 Identifying Health Disparities
   • 3.2 Tailoring Public Health Interventions
   • 3.3 Tracking Disease Outbreaks and Transmission
   • 3.4 Informing Policy and Resource Allocation
   • 3.5 Evaluating Program Effectiveness
4. Challenges in Collecting Group-Specific Data
5. Conclusion: Data Equity for Health Equity
6. References

Understanding the CDC's Mission: Data as the Foundation#

The CDC's work is guided by the principle that "data drives decisions." Every public health action—from vaccine distribution to disease prevention campaigns—relies on accurate, timely, and representative data. Without data, the agency cannot identify health threats, measure progress, or allocate resources effectively.

But data alone is not enough. To truly protect all Americans, the CDC must go beyond aggregated statistics (e.g., "national flu rates") and drill down into subgroups. This is because health risks, access to care, and outcomes vary dramatically across populations. For example, a disease might affect urban populations differently than rural ones, or young adults differently than seniors. By collecting data on these groups, the CDC ensures its strategies are inclusive and targeted.

The Importance of Disaggregated Data: Beyond "Average" Health#

Aggregated data (data combined across all groups) can mask critical differences. Imagine a scenario where national COVID-19 hospitalization rates appear low, but when broken down by race, Black and Latino communities have rates 2–3 times higher than white communities. Aggregated data would hide this disparity, leading to a false sense of progress.

Disaggregated data—data broken down by characteristics like race, ethnicity, age, gender, income, or geography—reveals these gaps. It allows the CDC to answer questions like:

• Which groups are most at risk for a disease?
• Why are some groups experiencing worse outcomes?
• Where are resources (e.g., clinics, vaccines) most needed?

In short, disaggregated data is the key to addressing health equity—the idea that everyone deserves a fair opportunity to be healthy.

Key Reasons the CDC Collects Data on Different Groups#

3.1 Identifying Health Disparities#

Health disparities are preventable differences in health outcomes between groups. These gaps often stem from social determinants of health (SDOH), such as poverty, lack of access to healthcare, systemic racism, or limited education. The CDC collects group-specific data to identify these disparities and understand their root causes.

Example: The CDC's National Center for Health Statistics (NCHS) tracks infant mortality rates by race. According to 2022 data, the infant mortality rate for non-Hispanic Black infants was 10.9 deaths per 1,000 live births, compared to 4.5 for non-Hispanic white infants—nearly 2.5 times higher. This disparity is linked to factors like maternal stress, lack of prenatal care, and housing instability—insights that drive targeted interventions to support Black mothers and families.

3.2 Tailoring Public Health Interventions#

Not all populations respond to the same public health messages or interventions. For example, a smoking cessation campaign targeting teens might use social media and peer influencers, while a campaign for older adults might rely on community centers and healthcare provider outreach.

By understanding the unique needs of groups, the CDC can design interventions that are culturally relevant, accessible, and effective.

Example: During the COVID-19 pandemic, the CDC used data on vaccine hesitancy among Latino communities to develop culturally tailored outreach. This included partnering with trusted community leaders, providing materials in Spanish, and hosting vaccination clinics at local churches and markets—efforts that increased vaccine uptake in this group.

3.3 Tracking Disease Outbreaks and Transmission#

Disease outbreaks often start in specific groups before spreading more widely. For example, measles outbreaks frequently begin in unvaccinated children, while HIV disproportionately affects men who have sex with men (MSM) and Black/African American communities.

By monitoring data from these high-risk groups, the CDC can detect outbreaks early, contain spread, and prevent larger epidemics.

Example: During 2018–2019, a measles outbreak in New York City primarily affected unvaccinated children in Orthodox Jewish communities. The CDC used data on vaccination rates and case locations to target outreach, including free clinics and educational materials in Yiddish, ultimately containing the outbreak.

3.4 Informing Policy and Resource Allocation#

Public health policies and funding decisions rely on evidence. Group-specific data helps the CDC and policymakers prioritize resources for the populations most in need.

Example: Data showing higher rates of diabetes in rural areas (due to limited access to healthy food and healthcare services) led the CDC to expand the National Diabetes Prevention Program (National DPP) in underserved communities, providing grants to local health departments and community organizations to address these gaps.

3.5 Evaluating Program Effectiveness#

Once interventions are implemented, the CDC uses group-specific data to measure their success. Did a program reduce teen pregnancy rates? Did a flu vaccine campaign increase uptake in older adults? Without data on these groups, it's impossible to know if interventions are working—or if they need adjustment.

Example: The CDC's reproductive health programs collect data on pregnancy rates by age, race, and geographic region to evaluate whether initiatives such as sex education and contraception access are reducing teen pregnancies in target groups.

Challenges in Collecting Group-Specific Data#

While group-specific data is critical, collecting it is not without challenges:

• Privacy Concerns: Collecting data on sensitive characteristics (e.g., race, sexual orientation) requires strict privacy protections to avoid discrimination or stigma.
• Underreporting: Marginalized groups (e.g., homeless populations, undocumented immigrants) may be underrepresented in data due to fear of repercussions or lack of access to healthcare.
• Standardization: Definitions of groups (e.g., "rural" vs. "urban") can vary, making it hard to compare data across regions.
• Resource Limitations: Collecting and analyzing disaggregated data requires funding, trained staff, and advanced technology—resources not always available in all communities.

The CDC addresses these challenges through partnerships with local health departments, community organizations, and researchers, and by adhering to strict ethical guidelines for data collection.

Conclusion: Data Equity for Health Equity#

The CDC's focus on collecting data from different groups is not just a technical exercise—it's a commitment to health equity. By understanding how health risks and outcomes vary across populations, the agency can create targeted, effective strategies that protect everyone, regardless of race, age, income, or location.

In a world where health disparities persist, disaggregated data is more than a tool—it's a lifeline. It empowers the CDC to turn information into action, ensuring that no group is left behind.

References#

• Centers for Disease Control and Prevention (CDC). Health Equity Science: Data to Inform Health Equity Actions. https://www.cdc.gov/health-equity-chronic-disease/hcp/health-equity-science/surveillance-data.html
• CDC. COVID-19 Vaccine Equity for Racial and Ethnic Minority Groups. https://archive.cdc.gov/www_cdc_gov/coronavirus/2019-ncov/community/health-equity/vaccine-equity.html
• National Center for Health Statistics (NCHS). Infant Mortality. https://www.cdc.gov/maternal-infant-health/infant-mortality/index.html
• CDC. Notes from the Field: Measles Outbreaks from Imported Cases in Orthodox Jewish Communities — New York and New Jersey, 2018–2019. https://www.cdc.gov/mmwr/volumes/68/wr/mm6819a4.htm
• CDC. Social Determinants of Health (SDOH). https://www.cdc.gov/about/priorities/why-is-addressing-sdoh-important.html
• CDC. About Teen Pregnancy. https://www.cdc.gov/reproductive-health/teen-pregnancy/index.html