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

TL;DR

This paper presents SHAPE, a spatial microsimulation framework using hierarchical IPF for small-area estimation of health outcomes, validated with CDC and BRFSS data, offering an accessible tool for public health analysis.

Contribution

Introduces SHAPE, an open-source R-based spatial microsimulation framework that improves small-area health outcome estimation with validated accuracy.

Findings

SHAPE's estimates moderately correlate with BRFSS data (r ~ 0.5).

SHAPE's estimates strongly align with CDC PLACES model estimates (r ~ 0.7-0.8).

SHAPE is open, reproducible, and suitable for public health applications.

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.