Bayesian modeling of spatial ordinal data from health surveys

Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this ar...

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Bibliographic Details
Published in:Statistics in medicine 2024-09, Vol.43 (21), p.4178-4193
Main Authors: Beltrán‐Sánchez, Miguel Ángel, Martinez‐Beneito, Miguel‐Angel, Corberán‐Vallet, Ana
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Female
Health Status Indicators
Health surveillance
Health surveys
Health Surveys - statistics & numerical data
Humans
individual‐level model
Likelihood Functions
Male
Models, Statistical
ordinal data analysis
post‐stratification
Public health
Small-Area Analysis
Spain - epidemiology
Spatial Analysis
spatial statistics
survey‐based studies
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Summary:Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual‐level model for small‐area estimation of survey‐based health indicators. A categorical likelihood is used at the first level of the model hierarchy to describe the ordinal data, and spatial dependence among small areas is taken into account by using a conditional autoregressive distribution. Post‐stratification of the results of the proposed individual‐level model allows extrapolating the results to any administrative areal division, even for small areas. We apply this methodology to describe the geographical distribution of a self‐perceived health indicator from the Health Survey of the Region of Valencia (Spain) for the year 2016.
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.10166