Analyzing Demographics Within Rural Populations

Overview

Rural populations are frequently treated as a single analytic group in public health datasets and reports. However, this broad classification can conceal differences and nuances within rural communities themselves. Disaggregating rural data helps uncover subgroup-specific patterns in disease burden, health behaviors, social determinants of health, and access to care. These insights are crucial for designing effective public health programs, allocating resources efficiently, and monitoring progress over time.

This tip sheet helps support the CDC's Office of Rural Health priorities and broader federal public health outcome goals. It encourages context-specific approaches that go beyond rural versus urban comparisons and look within rural populations to identify specific risk factors, protective factors, and opportunities for intervention. Identifying and addressing gaps in data visibility strengthens the scientific foundation for effective and community-specific rural health policy and practice.

Why This Matters

Rural populations in the United States are diverse and multidimensional. Treating them as a homogeneous group can miss differences in health outcomes, risk factors, and access to care. Disaggregated data:

1. Reveal disparities by race/ethnicity, age, geography, income, sex, and more.
2. Support results-driven principles that CDC’s Office of Rural Health prioritizes.
3. Improve targeting and tailoring of interventions for underserved or high-risk groups.
4. Enable resources to be directed where they are most needed via precision public health.

When to Disaggregate

Disaggregating rural data helps identify differences in sub-groups, tailor interventions, and generate new hypotheses. Consider disaggregating data when:

1. Subgroup-Specific Outcomes Are Policy-Relevant or Intervention-Driven.

If your program or intervention focuses on specific populations—such as rural veterans, Black maternal health, or older adults, disaggregated data help evaluate impact, justify funding, and guide resource allocation.

2. Disparities Are Known or Suspected.

Use disaggregation to confirm, measure, and better understand disparities previously documented in literature, surveillance systems, or community reports.

3. Data Quality Supports Reliable Subgroup Estimates.

Disaggregate when your dataset has sufficient sample size and precision to support meaningful subgroup analysis. Look for oversampled groups (e.g., AI/AN in PRAMS), pooled multi-year estimates, or modeled data (e.g., PLACES or NHIS modeling).

Consideration:

• Use stratification cautiously with small subgroups—combine years or regions if needed, and always present confidence intervals.

4. You're in the Hypothesis-Generating or Exploratory Phases of Research.

In the early stages of research, disaggregation can help identify emerging patterns or unusual trends that merit further study.

Considerations:

• Clearly label findings from exploratory disaggregation as hypothesis-generating rather than confirmatory and avoid over-interpretation.
• Use these results to inform future data collection, targeted surveillance, or analytic refinement rather than definitive conclusions.

5. Priority-Focused or Community-Centered Principles Require It.

Even when sample sizes are limited, disaggregation may be necessary to honor community-identified priorities, respond to stakeholder demands, or meet the goals of priority-focused programs.

Consideration:

• When disaggregating data based on community priorities, document the rationale and acknowledge any statistical limitations. Pair quantitative results with qualitative insights or community context to ensure findings are interpreted respectfully and used appropriately for planning and decision-making.

Standard Demographic Variables to Consider

To uncover meaningful variation within rural populations, it is essential to consider a range of demographic variables. Below are core variables that often reveal public health disparities when stratified, along with examples of their use in practice and how they can inform analyses.

1. Race and Ethnicity.

Race and ethnicity remain powerful predictors of health outcomes due to structural differences, historical disinvestment, and ongoing differences in healthcare access, quality, and structural drivers of health.

Considerations:

• Use Office of Management and Budget (OMB) categories at a minimum. Where possible, further disaggregate within broad groups (e.g., Mexican vs. Puerto Rican vs. Central American origin).
• Collaborate with Tribal Epidemiology Centers for Indigenous-specific analyses.

2. Age.

Rural areas generally have older populations, but age-specific trends can vary significantly and have implications for service delivery and prioritization of risk factors.

Considerations:

• Stratify at a minimum into children (0–17 years old), working-age adults (18–64 years old), and older adults (65 years old and above).
• Consider finer categories for youth suicide, maternal health, or aging-related conditions.

3. Sex.

Sex shapes health risks, health-seeking behaviors, care access, and exposures to violence and discrimination. Differences may be magnified in rural settings due to the scarcity of specialized providers and cultural stigma.

Consideration:

• Differentiate between sex when data is available.

4. Income and Poverty Status.

Economic deprivation is a powerful determinant of health and is widespread in rural areas, particularly in persistent poverty counties (i.e., counties with ≥20% poverty over the past 30 years)9.

Considerations:

• Use federal poverty level (FPL), income-to-poverty ratio, or other income brackets.
• Consider coupling with employment status and occupation for more granularity.

5. Educational Attainment.

Education influences health literacy, employment, and health outcomes, and is often lower in rural areas, particularly among older adults and in the southern United States, including Appalachia.

Considerations:

• Commonly grouped as: less than high school, high school diploma/GED, some college, and bachelor's degree or higher.
• Combine age with other factors to examine generational patterns in health behaviors.

6. Disability Status.

Rural adults with disabilities face significant challenges in mobility, transportation, access to care, and social participation which can impact physical and mental health.

Considerations:

• Use functional limitation categories where available (e.g., cognitive, ambulatory).
• Assess interaction with poverty, isolation, and aging.

7. Immigrant and Refugee Status.

Rural immigrant communities—such as farmworkers, meatpacking workers, or refugee resettlement populations—may face compounding barriers related to documentation, language, and discrimination.

Considerations:

• It may only be available in qualitative or programmatic data, not in national surveys.
• Consider using the language spoken at home or the country of birth as proxies.

8. Geographic Subtypes.

Not all rural areas are the same. Disaggregating by geographic subtypes can reveal structural and regional disparities often obscured by broad metro/nonmetro categories.

Considerations:

• Use classifications like frontier vs. non-frontier, Appalachian vs. Delta vs. non-Appalachian, and persistent poverty status.
• Combine geography with race/ethnicity for place-based insights.

How to Do It: Practical Considerations

Disaggregating rural data requires both technical skill and thoughtful interpretation. Below are key considerations to ensure your analysis is statistically sound, meaningful, and outcome-focused:

1. Data Suppression and Small Numbers

• Disaggregated data often results in small cell sizes, which can compromise both privacy and statistical reliability.
• To assess precision, use suppression rules (e.g., n < 10) and apply confidence intervals or relative standard errors to ensure accurate results.
• To improve stability, aggregate multiple years of data (e.g., 3- or 5-year pooled estimates) to reduce the variability of the estimates.
• When direct estimates are unstable, consider using Bayesian smoothing or modeled estimates (e.g., CDC PLACES or IHME).

2. Visualization

• Effective visualizations can uncover patterns that tables alone may obscure.
• To compare subgroups within rural counties, use stratified bar charts or side-by-side maps.
• To show variation across geographies without relying on urban comparisons, apply rural-only heatmaps.
• To show overlapping identities (e.g., race × poverty) for intersectional analysis, consider dot plots or faceted graphs.

3. Narrative Framing

• To avoid reinforcing stereotypes or pathologizing rural communities, interpret findings with care and clarity.
• Emphasize within-group variation, not just rural-urban gaps.
• Avoid deficit framing—contextualize disparities using social and structural drivers of health and historical disinvestment.
• Highlight community strengths and resilience where appropriate (e.g., kin networks, mutual aid, and informal caregiving).

4. Intersectionality

• Explore how multiple identities interact to shape health outcomes.
• Examine race, income, and geography together (e.g., "rural Black women in persistent poverty counties").
• To understand how structural disadvantage accumulates across populations, layer variables where possible.

Cautions and Limitations

Disaggregated data within rural populations offer valuable insights. However, you must break down data carefully, transparently, and ethically. Consider this guidance as you plan and interpret subgroup analyses:

1. Avoid Overinterpreting Sparse Data

• Small sample sizes are common when disaggregating rural data, particularly for historically underrepresented subgroups (e.g., AI/AN or immigrant populations).
• When data is too sparse to produce reliable or meaningful estimates, do not force disaggregation.
• Always report confidence intervals, relative standard errors (RSE), and other measures of precision.
• If subgroup estimates are exploratory or unstable, clearly label them as hypothesis-generating or descriptive only.

2. Acknowledge Ecological Fallacy Risks

• Many rural health data sources, such as CDC WONDER or, are available at the county or ZIP code level, but not at the individual level.
• Be cautious about inferring individual-level associations from aggregate data.
• For interpretation, consider combining geographic data with qualitative insights or community-level knowledge.

3. Don’t Pathologize Subgroups

• If not framed carefully, data disaggregation can unintentionally reinforce narratives of deficiency or dysfunction in certain rural populations.
• Shift the focus from individual "risk behaviors" to structural and systemic factors—such as underinvestment, racism, policy neglect, or service shortage areas.
• Avoid language that blames communities. Instead, highlight context, root causes, and opportunities for support.

4. Recognize Structural Data Gaps

• Some subgroups may be invisible or misrepresented in standard datasets due to underreporting, misclassification, or the limited number of categories.
• AI/AN populations are often misclassified in vital records.
• Disability type and immigration status are rarely collected in large national surveys.
• When quantitative data is limited, work with community partners and qualitative data to fill contextual gaps.

5. Ethical Considerations and Community Accountability

• Disaggregated data may reveal sensitive or stigmatizing findings. Ensure that your analysis aligns with community priorities and leverages appropriate review and feedback mechanisms.
• When possible, engage affected communities in the interpretation and dissemination of subgroup findings.
• Respect Tribal data sovereignty, particularly when analyzing AI/AN populations. Follow HHS Office of the Chief Data Officer Policies and Processes for Tribal Data.

Key Takeaways

• Rural populations are diverse. Disaggregation by race, age, income, and other demographics can reveal critical within-group differences often hidden in rural-urban comparisons.
• Use disaggregation when it adds value. Prioritize it when informing policy, addressing priority populations, or identifying high-need subgroups—especially when data quality supports it.
• Consider multiple dimensions. Race, sex, poverty, disability, geography, and intersectionality all influence rural health outcomes and access to care.
• Balance rigor and relevance. To ensure data are meaningful, reliable, and appropriately framed, use pooled estimates, confidence intervals, and visualization strategies.
• Interpret ethically. Avoid deficit-based narratives and ecological fallacies. Emphasize structural context over individual blame.
• Acknowledge limitations. Some subgroups may be undercounted or misclassified. Ensure transparent reporting and community-informed interpretation.
• Addressing differences requires visibility. Disaggregation helps make rural differences visible, actionable, and solvable—and supports CDC's broader public health goals.

Conclusion

Disaggregating rural data by demographic subgroups can be a powerful tool for advancing visibility and precision in public health. Yet with this power comes responsibility—to analyze ethically, interpret contextually, and report transparently. When we acknowledge the strengths and limitations of our data, we ensure that our work accurately describes disparities and contributes to community-informed, just, and lasting solutions.

Download Printable Tip Sheet

Looking Within – Analyzing Demographics Within Rural Populations