Estimating County Level Health Indicators Using Spatial Microsimulation

Abstract

Given the importance of understanding health outcomes at fine spatial scales, iterative proportional fitting (IPF), a form of small area estimation, was applied to a fixed number of health-related variables (obesity, overweight, diabetes) taken from regionalized 2019 survey responses (n = 5474) from the Idaho Behavioral Risk Factor Surveillance System (BRFSS). Using associated county-level American Community Survey (ACS) census data, a set of constraints, which included age categorization, race, sex, and education level, were used to create county-level weighting matrices for each variable, for each of the seven (7) Idaho public health districts. Using an optimized modeling construction technique, we identified significant constraints and grouping splits for each variable/region, resulting in estimates that were internally and externally validated. Externally validated model results for the most populated counties showed correlations ranging from .79 to .85, with p values all below .05. Estimates indicated higher levels of obesity and overweight individuals for midsouth and southwestern Idaho counties, with a cluster of higher diabetes estimates in the center of the state (Gooding, Lincoln, Minidoka, and Jerome counties). Alternative external sources for health outcomes aligned extremely well with our estimates, with wider confidence intervals in more rural counties with sparse populations.

Keywords: Diabetes; Idaho; Iterative Proportional Fitting; Obesity; Overweight; Small Area Estimates.

Fig 1.

Idaho Study Area. Idaho’s seven (7) health districts served as the basis for our spatial modeling framework. A separate model was constructed for each district (modified from IDHW - https://healthandwelfare.idaho.gov/)

Fig 2.

Small Area Estimation Methodology. For each model type and variable (obesity, overweight, and diabetes), a modularized set of R code was run to integrate BRFSS survey and ACS data, construct weighting matrices, as well as perform imputation, integerisation, expansion, and validation. Computational analysis utilized the R package mipfp (Lovelace and Dumont, 2016).

Fig 3.

Optimal model results. The optimal model given internal and external validation (dynamic base) for all three variables (a - obesity, b - overweight, and c - diabetes). Maps for all models can be found in the supplemental materials.