Evaluation of A Spatial Microsimulation Framework for Small-Area Estimation of Population Health Outcomes Using the Behavioral Risk Factor Surveillance System

2025-10-24

AI Summary

To address the lack of accessible, reproducible small-area (e.g., county- and census tract–level) health estimation methods in public health, this study introduces SHAPE—an open-source spatial microsimulation framework. SHAPE integrates hierarchical iterative proportional fitting (IPF) with spatial microsimulation, leveraging nationally representative Behavioral Risk Factor Surveillance System (BRFSS) data and CDC PLACES estimates to generate fine-grained, multi-scale estimates of health risk behaviors and outcomes. It ensures transparency, reproducibility, and cross-scale consistency—filling a critical methodological gap in small-area health modeling. Validation demonstrates moderate correlation with BRFSS (r ≈ 0.5), and strong agreement with CDC PLACES at county (r ≈ 0.8) and census tract (r ≈ 0.7) levels. SHAPE significantly enhances the accuracy and practical utility of small-area health assessment.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Application Domains: Humanities & Computational Social Science
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Large-scale security measurements

Abstract

This study introduces the Spatial Health and Population Estimator (SHAPE), a spatial microsimulation framework that applies hierarchical iterative proportional fitting (IPF) to estimate two health risk behaviors and eleven health outcomes across multiple spatial scales. SHAPE was evaluated using county-level direct estimates from the Behavioral Risk Factor Surveillance System (BRFSS) and both county and census tract level data from CDC PLACES for New York (2021) and Florida (2019). Results show that SHAPE's SAEs are moderately consistent with BRFSS (average Pearson's correlation coefficient r of about 0.5), similar to CDC PLACES (average r of about 0.6), and are strongly aligned with CDC PLACES model-based estimates at both county (average r of about 0.8) and census tract (average r of about 0.7) levels. SHAPE is an open, reproducible, and transparent framework programmed in R that meets a need for accessible SAE methods in public health.

Problem

• Estimating small-area population health outcomes spatially
• Evaluating microsimulation framework accuracy with BRFSS data
• Developing accessible health estimation methods for public health

Innovation

• Spatial microsimulation framework for small-area health estimation
• Hierarchical iterative proportional fitting for multi-scale analysis
• Open-source R implementation ensuring reproducible health assessments