Triple-Burden Health Risk Mapping Project Ideas

ISEF Category: Translational Medical Science

Subcategory: Disease Prevention  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

Some neighborhoods face three health hits at once. Food options are limited, air quality is worse, and primary care is harder to reach. That mix can quietly push cardiometabolic risk higher. You can turn those patterns into a map, then test which small fix changes the picture most.

What Is It?

This project asks you to combine public data sets to find places where health risk stacks up. Think of each census tract as a report card. One grade comes from food access, one from air pollution, and one from access to primary care. When all three are weak, the tract may face a higher burden than any single measure shows on its own.

The core idea is simple. You are not diagnosing people. You are looking for patterns in place. CDC PLACES gives modeled health estimates, the USDA Food Access Atlas shows where healthy food is harder to reach, and EPA AirNow can help you compare air quality exposure. Together, these sources let you ask where risk clusters, and which change, like better food access, cleaner air, or closer clinics, would move the score most.

Why This Is a Good Topic

This is a strong science fair topic because the data already exist, the question is testable, and the analysis has real public health value. You can compare neighborhoods, build a scoring system, and test whether one intervention lowers predicted risk more than another. You will also learn map reading, data cleaning, variable weighting, and how to explain uncertainty in a model. That makes the project feel real, not just descriptive.

Research Questions

• How does adding air quality data change which census tracts count as high risk compared with using food access alone?
• What is the effect of adding primary-care access as a third layer on the number of triple-burden census tracts?
• Does weighting food access, air pollution, and clinic access equally change the final risk ranking of census tracts?
• To what extent do different marginal interventions, such as better food access or lower pollution, shift predicted cardiometabolic risk scores?
• Which census tract features best predict high modeled cardiometabolic risk when all three exposures are analyzed together?
• How does the triple-burden score vary between urban, suburban, and rural census tracts?

Basic Materials

• Laptop or desktop computer.
• Spreadsheet software such as Google Sheets or Excel.
• Free GIS software such as QGIS.
• CDC PLACES data tables.
• USDA Food Access Research Atlas data.
• EPA AirNow air quality data or archived AQI data.
• Census tract shapefiles from the U.S. Census Bureau TIGER/Line files.
• Color printer or online map export tool for figures.
• Notebook for tracking variable definitions and codebook notes.

Advanced Materials

• Laptop or desktop computer with enough memory for geospatial files.
• QGIS or ArcGIS Pro if your lab provides a license.
• Python with pandas, geopandas, matplotlib, seaborn, and scikit-learn.
• R with sf, tmap, ggplot2, and dplyr.
• CDC PLACES small-area estimate files.
• USDA Food Access Research Atlas data.
• EPA AirNow or EPA Air Quality System data.
• Census tract boundary files and county boundary files.
• ACS social and demographic covariates for adjustment.
• Optional local clinic directory or HRSA health center location data.

Software & Tools

• QGIS: Builds tract maps, joins public health layers, and visualizes hotspot patterns.
• Google Sheets: Cleans small tables and helps you compare tract-level variables.
• Python: Merges data files, calculates composite risk scores, and runs basic statistical tests.
• R: Fits regression models and makes publication-style plots for your results.
• ImageJ: Can measure colors or annotations if you export map figures for analysis.

Experiment Steps

1. Define your unit of analysis, then decide whether you will study one county, one state, or a national sample of census tracts.
2. Choose one clear outcome, such as modeled cardiometabolic risk, and one way to score each exposure layer.
3. Build a data dictionary so you know exactly how food access, air pollution, and clinic access will be measured.
4. Join the public data sets to tract boundaries, then check for missing values, mismatched geography, and duplicate records.
5. Test at least one weighting scheme, then compare it with a second scheme to see whether your hotspot map changes.
6. Plan a validation step that compares your composite score with an external health or demographic measure.

Common Pitfalls

• Mixing data from different years, which can make a tract look high risk for the wrong reason.
• Using county-level air quality when the rest of the project uses census tracts, which blurs local variation.
• Treating raw AirNow readings as if they match long-term exposure, which can distort the pollution layer.
• Forgetting that food access and clinic access can overlap, which can make two variables look independent when they are not.
• Building a score without testing sensitivity, which leaves you unable to explain whether one changed assumption flips the ranking.

What Makes This Competitive

A stronger project does more than make a pretty map. It tests whether your result holds up when you change the scoring method, the data year, or the way you define access. You can also compare simple hotspot rules with a regression model or clustering approach. If your final analysis explains which intervention shifts risk the most, and why, your project starts to look like real population health research.

Project Variations

• Focus on one state or metro area and compare urban, suburban, and rural tracts.
• Swap in asthma or diabetes prevalence from CDC PLACES as the health outcome instead of a combined cardiometabolic score.
• Add a fourth layer, such as broadband access or transportation access, to test whether the triple-burden pattern gets stronger.

Learn More

• CDC PLACES: Search the CDC PLACES site for small-area estimates of chronic disease and risk factors by census tract.
• USDA Food Access Research Atlas: Find tract-level food access measures on the USDA Economic Research Service site.
• EPA AirNow: Use EPA AirNow for current and historical air quality information and background on AQI.
• U.S. Census Bureau TIGER/Line Files: Download census tract boundary shapefiles from the Census Bureau site for mapping.
• NIH PubMed: Search review articles on food access, air pollution, primary care access, and cardiometabolic risk.
• MIT OpenCourseWare: Look for free geospatial analysis and public health data science materials to build mapping skills.