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Introduction: The objective of this research was to spatially analyze linked health data for geographic trends in factors impacting children's health. Traditional linear regression analyses of countylevel data tend to inflate R2. Spatial regression represents a robust approach for improved analysis of geographic data. Methods: We used GeoDa 1.6.0 to regress 3,221 U.S. county-level child health outcomes (e.g., infant mortality, child mortality) on independent variables (e.g., low birth weight, percent race/ethnicity, uninsured, emotional support). Statistical analyses included spatial R2, Moran's I, and multicollinearity measures. The data source was the 2014 County Health Rankings. Results: Three spatial regression models (health, socioeconomic, and combined) were compared for infant and child mortality. The combined model for infant mortality rate yielded the largest adjusted R2 = 0.428 (F = 110.9, p < 0.001), similarly for child mortality rate R2 = 0.411 (F =94.3, p < 0.001). The strongest predictors in bothmodels were obesity, smoking, teen birth rate, severe housing problems, no social supports, and urbanicity. Discussion: The results demonstrate correlations between county-level conditions and child health outcomes, supporting previous research linking poor health/education and low socioeconomic conditions. Geospatial information can assist policymakers to apply health education interventions. [ABSTRACT FROM AUTHOR]