Modeling Community Health with Areal Data: Bayesian Inference with Survey Standard Errors and Spatial Structure

• Main Authors: Connor Donegan, Yongwan Chun, Daniel A. Griffith
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: spatial epidemiology; health disparities; Bayesian inference; mortality rates; measurement error; spatial autocorrelation
• Online Access: https://www.mdpi.com/1660-4601/18/13/6856

Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider probable observational error may have substantial impact on the large bodies of literature relying on small-area estimates, including inferential biases and over-confidence in results. The issue is particularly salient for predictive models employed to prioritize communities for service provision or funding allocation. Leveraging the tenets of plausible reasoning and Bayes’ theorem, we propose a conceptual framework and workflow for spatial data analysis with areal survey data, including visual diagnostics and model specifications. To illustrate, we follow Krieger et al.’s (2018) call to routinely use the Index of Concentration at the Extremes (ICE) to monitor spatial inequalities in health and mortality. We construct and examine SEs for the ICE, use visual diagnostics to evaluate our observational error model for the ICE, and then estimate an ICE–mortality gradient by incorporating the latter model into our model of sex-specific, midlife (ages 55–64), all-cause United States county mortality rates. We urge researchers to consider data quality as a criterion for variable selection prior to modeling, and to incorporate data reliability information into their models whenever possible.