Bayesian Hierarchical Spatial Model for Small Area Estimation with Non-ignorable Nonresponses and Its Applications to the NHANES Dental Caries Assessments

TL;DR

This paper introduces a Bayesian hierarchical spatial model tailored for small area estimation of dental caries data from NHANES, effectively handling complex survey design, spatial correlations, and non-ignorable nonresponses.

Contribution

The study develops a novel Bayesian hierarchical spatial model with a Potts structure and non-ignorable missing data adjustment for improved small area estimation in dental health surveys.

Findings

Strong spatial associations between teeth and surfaces identified

Dental hygienic factors like fluorosis and sealants reduce disease risk

Model effectively accounts for complex survey design and nonresponse

Abstract

The National Health and Nutrition Examination Survey (NHANES) is a major program of the National Center for Health Statistics, designed to assess the health and nutritional status of adults and children in the United States. The analysis of NHANES dental caries data faces several challenges, including (1) the data were collected using a complex, multistage, stratified, unequal-probability sampling design; (2) the sample size of some primary sampling units (PSU), e.g., counties, is very small; (3) the measures of dental caries have complicated structure and correlation, and (4) there is a substantial percentage of nonresponses, for which the missing data are expected to be not missing at random or non-ignorable. We propose a Bayesian hierarchical spatial model to address these analysis challenges. We develop a two-level Potts model that closely resembles the caries evolution process and captures complicated spatial correlations between teeth and surfaces of the teeth. By adding Bayesian hierarchies to the Potts model, we account for the multistage survey sampling design and also enable information borrowing across PSUs for small area estimation. We incorporate sampling weights by including them as a covariate in the model and adopt flexible B-splines to achieve robust inference. We account for non-ignorable missing outcomes and covariates using the selection model. We use data augmentation coupled with the noisy exchange sampler to obtain the posterior of model parameters that involve doubly-intractable normalizing constants. Our analysis results show strong spatial associations between teeth and tooth surfaces and that dental hygienic factors, fluorosis and sealant reduce the risks of having dental diseases.