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Predicting the Complex, Multilevel Ecology of US County-Level Diabetes Prevalence: A Cross-Sectional, Artificial
Nicolaas P Pronk1,2,3, Shuaijie Wang3,4, Richard Bergenstal1,5
1HealthPartners Institute, Minneapolis, Minnesota, USA.
Diabetes/Metabolism Research and Reviews
|June 2, 2026
Summary
Predicting diabetes prevalence requires understanding its complex ecological factors. Key predictors include physical inactivity, minority status, distress, and obesity, highlighting the need for multifaceted interventions.
Area of Science:
- Public Health
- Epidemiology
- Data Science
Background:
- Diabetes prevalence is influenced by a complex interplay of upstream social/economic factors and downstream individual risk factors.
- Understanding this multilevel ecology is crucial for accurate diabetes prediction.
- Previous models have not fully accounted for the contextual complexity of diabetes risk.
Purpose of the Study:
- To predict diabetes prevalence at the United States county level.
- To identify key upstream and downstream predictors of diabetes using advanced analytical methods.
- To account for the multilevel ecological factors influencing diabetes.
Main Methods:
- Cross-sectional analysis of US county-level data with 27 predictor variables.
- Development and evaluation of a Light Gradient Boosting Machine (LightGBM) model.
- Assessment of model performance and feature importance using Shapley (SHAP) values.
Main Results:
- The LightGBM model explained 95% of the variance in county-level diabetes prevalence (R² = 0.952).
- Seventeen features were retained in the final predictive model.
- Highest predictor importance was associated with physical inactivity, racial/ethnic minority status, frequent physical distress, and obesity.
Conclusions:
- Physical inactivity, minority status, distress, and obesity are significant predictors of diabetes prevalence.
- Effective diabetes reduction strategies must address both upstream and downstream factors.
- Interventions should prioritize physical activity, obesity management, health equity, and cultural considerations.
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