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

ABSTRACT: 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 plaus

SUBMITTER: Donegan C 

PROVIDER: S-EPMC8297362 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature