Data visualization can make differences in diabetes prevalence across income groups easier to see, compare and investigate. It cannot show, by itself, that income causes diabetes. In a CDC comparison of U.S. adults in 2021, diagnosed-diabetes prevalence was 16.4% in the household-income group below $35,000, 11.0% from $35,000 to under $75,000, and 7.7% at $75,000 or more. Those estimates show a substantial association—not a causal effect.
What the CDC income comparison shows
The CDC’s 2021 State Burden Toolkit reports diagnosed-diabetes prevalence among adults by household-income category:
| Household income category | Diagnosed diabetes prevalence | 95% confidence interval |
|---|---|---|
| Below $35,000 | 16.4% | 15.8%–16.9% |
| $35,000 to under $75,000 | 11.0% | 10.6%–11.5% |
| $75,000 or more | 7.7% | 7.3%–8.0% |
In this specific comparison, the difference between the lowest and highest income categories is 8.7 percentage points. Dividing 16.4 by 7.7 gives a descriptive prevalence ratio of about 2.1: diagnosed-diabetes prevalence in the lowest category was about 2.1 times that in the highest. These are estimates for adults and diagnosed diabetes, not lifetime probabilities or measures of every diabetes case. The figures do not establish that income alone produced the difference. CDC State Burden Toolkit health-burden results
A chart should show the estimates and their confidence intervals, not just a downward trend line. The intervals describe uncertainty around the estimates; they do not account for every source of bias or explain why the groups differ.
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Define diabetes, income and the unit of analysis
Choose a diabetes measure
“Diabetes rate” is ambiguous. Prevalence is the share of a population living with a condition; incidence counts new cases over a defined period. Hospitalizations, complications, mortality and costs are different outcomes. The CDC comparison above concerns diagnosed diabetes among adults. BRFSS-based measures rely on respondents reporting that a health professional has told them they have diabetes, so they do not count all undiagnosed cases. Broad adult surveillance may also not distinguish type 1 from type 2 diabetes.
Diagnosis depends partly on access to screening and health care. A lower diagnosed prevalence could reflect lower underlying disease, less diagnosis, or both. State what the measure captures in the chart title or subtitle.
Choose an income measure
Household-income categories, individual income, family income, poverty rate, income-to-poverty ratio and area median household income are not interchangeable. The CDC example uses broad household-income brackets. They are ordered groups, not evenly spaced values: the top category has no stated upper limit, so it cannot be treated as a precise numeric interval.
If income is measured for a county or census tract, it describes the area, not each resident. A person with low income may live in a high-income county, and an affluent person may live in a lower-income county.
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- Individual-level analysis compares people using their own diabetes and income data. It can adjust for other characteristics, but survey responses, missing income data and sampling limits matter.
- Area-level analysis compares places, such as counties, using summarized diabetes and income measures. It is useful for maps and service planning, but it cannot establish that the individuals with lower incomes are the same individuals with diabetes. That mistaken leap is the ecological fallacy.
CDC county diabetes estimates can be modeled rather than direct, equally precise counts for every county. The CDC describes the estimation approach and geographic patterns in its county diagnosed-diabetes data and maps. Check whether a chosen measure is modeled, survey-based, crude or age-adjusted before comparing locations.
Choose a chart that fits the question
| Visualization | Best use | Risk | Safeguard |
|---|---|---|---|
| Bar chart | Comparing a few income categories | A truncated axis can exaggerate differences | Use a clear baseline and show confidence intervals |
| Dot plot with intervals | Comparing estimates and uncertainty across groups or places | Some readers may be less familiar with it | Use direct labels and a plain-language caption |
| Scatterplot | Comparing area prevalence with continuous income or poverty | Area-level patterns can be misread as individual-level effects | Label each point as a place and explain the unit |
| Choropleth map | Showing where rates vary geographically | Large areas may dominate visually; similar maps may imply a relationship | Map rates, explain uncertainty and pair maps with a scatterplot |
| Small multiples | Comparing patterns by age, sex, race and ethnicity, region or rurality | Too many panels can overwhelm readers | Use a consistent scale and a limited set of meaningful groups |
| Time series | Tracking whether an income gap changes over time | Survey changes can make years incomparable | Use consistent definitions and mark methodological breaks |
| Dashboard | Allowing exploration across places, years and subgroups | Filters can hide the main result or invite incompatible comparisons | Keep filters visible and show definitions, year and estimate type |
Use a bar chart or dot plot for income groups
For a small set of categories, plot prevalence on the vertical axis and income category on the horizontal axis. Add 95% confidence intervals and label the population, measure and year. A dot plot often keeps attention on the estimate rather than the filled area of a bar. If using bars, avoid an axis that makes modest differences look dramatic.
Use a scatterplot for continuous area measures
For a county-level comparison, put county poverty rate or median household income on the horizontal axis and county diagnosed-diabetes prevalence on the vertical axis. Each point represents a county, not a person. A fitted line can summarize association, but it is not evidence of causation. If point size represents population, explain that encoding; label only informative outliers rather than every county.
Use maps to show place, not prove a relationship
A pair of maps—one for diabetes prevalence and one for income or poverty—can show geographic overlap. Similar color patterns do not establish that income explains the diabetes pattern. Pair maps with a scatterplot or table, use rates rather than raw counts for place-to-place comparisons, and label whether rates are age-adjusted. A county’s physical size can also draw attention unrelated to the number of residents affected.
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Find and combine credible U.S. data
- For national, state and county indicators: CDC’s diabetes data and statistics page describes the U.S. Diabetes Surveillance System. Its associated surveillance dataset was listed as updated June 23, 2026; the update date is not necessarily the observation year for every indicator.
- For state-based adult trends: The CDC’s BRFSS prevalence data cover 2011 onward and are updated as annual data become available. They measure self-reported diagnosed diabetes, not a complete clinical registry.
- For state burden comparisons: The CDC Diabetes State Burden Toolkit includes health, economic and mortality information. Its health-burden dataset can be accessed through OData for tools such as spreadsheets or Tableau.
- For area income and poverty: Use the U.S. Census Bureau’s American Community Survey data. Select a specific table, release year and geography; the general data page does not itself specify which measure is right for a particular analysis.
Do not assume two datasets describe the same period merely because they appear together in a toolkit. The CDC toolkit’s health burden draws on 2020–2021 BRFSS sources, while its economic burden uses different source years, including 2014, 2021 and 2022. Label each variable’s year rather than presenting all values as a synchronized snapshot. CDC economic-burden dataset
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build a reproducible comparison
- Write a precise question. For example: “Among U.S. adults, how does diagnosed-diabetes prevalence vary by household-income category, and how does the pattern differ by age, race and ethnicity, sex and rurality?”
- Select data suited to the question. The CDC income-stratified 2021 toolkit figures are suitable for a clear descriptive graphic. Individual-level analysis requires appropriate survey microdata; a county map requires compatible county health and Census measures.
- Record definitions and methods. Note age range, diabetes measure, income definition, geographic unit, year, weighting, crude or adjusted status, estimation method, confidence-interval method, missing-data handling and suppression rules. The CDC’s toolkit technical documentation describes its data and subgroup methods.
- Harmonize before joining. Match geographic identifiers and boundaries, keep household income distinct from individual income, align years as closely as possible, and confirm that percentages share a denominator. If years differ, disclose the mismatch.
- Start descriptively. Plot the overall measure, then the income comparison with uncertainty. Add geographic or subgroup views only when they answer a defined question.
- Calculate interpretable gaps. Report percentage-point differences alongside any ratio. For the cited comparison, 16.4% minus 7.7% equals 8.7 percentage points; the prevalence ratio is about 2.1. Do not imply either is an adjusted or causal effect.
- Consider confounding and subgroup patterns. Examine age, sex, race and ethnicity, education, region and rurality. Depending on the question and data, methods can include age-standardization, stratification, survey-weighted regression or multilevel models for people within places. An inequality analysis may use measures such as the slope index of inequality; one CDC-linked analysis used education and family poverty-to-income ratio as socioeconomic measures. CDC-linked diabetes inequality analysis
- Publish the method with the graphic. Provide a caption with population, denominator, measure, year, source and estimate type. Share the underlying data or a reproducible calculation when appropriate, and preserve uncertainty rather than hiding it in a footnote.
Interpret the pattern without overstating it
Income is one socioeconomic dimension, not a complete explanation. Age composition, education, race and ethnicity, geography, rurality, insurance and health-care access can all affect comparisons. Potential pathways connecting socioeconomic conditions to diabetes include food affordability, housing stability, work schedules, transportation, chronic stress, neighborhood safety, preventive care, medication costs and access to diabetes education. Unless an analysis directly tests these pathways, present them as possible context—not established explanations for its observed difference.
Use language such as “higher diagnosed-diabetes prevalence was observed in the lower-income category” or “county poverty and diabetes prevalence were associated.” Avoid saying that income “caused” the difference, that a map “proves” inequality, or that higher income “protects” people. A descriptive chart can identify patterns, outliers and questions for further analysis; causal claims require an appropriate study design and assumptions beyond visualization.
Check for common visualization failures
- Ignoring age: Older populations generally have higher prevalence. Compare like age groups or use clearly labeled age-adjusted estimates.
- Mixing crude and adjusted rates: They answer different questions and can produce different rankings. Do not combine them without explicit labeling.
- Treating rankings as precise: Small-area estimates may be unstable, and intervals may overlap. Avoid declaring a “worst” county when the data do not support a meaningful distinction.
- Using raw counts to compare differently sized places: A larger population can have more cases without a higher prevalence. Use rates for prevalence comparisons; use counts when the question is total service demand.
- Ignoring survey design: Unweighted survey responses should not be presented as population estimates. Use published weighted estimates or correctly account for the survey design.
- Using a misleading color scale: Red-to-green palettes may imply good versus bad and can be difficult for some readers to distinguish. Use an ordered, accessible palette and include numeric context.
- Overloading a dashboard: A large number of controls can obscure the main result. State the key finding first, keep active filters visible and prevent comparisons across incompatible years or measures.
Choose tools for the analysis, not the other way around
The tool should follow the work required. A simple explanatory chart needs careful definitions and uncertainty, not necessarily an interactive dashboard. R or Python can support reproducible data preparation, survey analysis and custom charts; Excel can handle preliminary tables and simple comparisons; Datawrapper can help produce publication-ready static graphics; Tableau or Power BI may suit a genuine interactive dashboard, especially where an organization already supports the platform. For rigorous analysis, keep data cleaning and statistical calculations in a documented workflow rather than relying on a visualization interface alone.
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Public CDC and Census data do not make every derived dataset safe to publish: do not upload identifiable health records to a public visualization service, and review privacy, suppression and governance requirements before sharing data.
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