🤖 AI Summary
This study addresses the challenge of spatially joint modeling of multivariate ordinal data across multiple health domains in population surveys. We propose a novel multivariate ordinal spatial model that integrates individual-level covariate effects with hierarchical spatial dependence structures. Methodologically, the model combines a latent multivariate Gaussian process with an ordinal regression framework to simultaneously capture intra-domain variable correlations, individual heterogeneity, geographic spatial clustering, and cross-variable spatial covariance. Applied to mental health indicators from the 2022 Valencia (Spain) Health Survey, the model substantially improves spatial pattern estimation accuracy for all outcomes and identifies geographically coherent clusters exhibiting cross-indicator associations of public health relevance. To our knowledge, this is the first scalable and interpretable statistical framework enabling spatially coherent analysis of high-dimensional ordinal survey data.
📝 Abstract
Health surveys provide valuable information for monitoring population health, identifying risk factors and informing public health policies. Most of the questions included are coded as ordinal variables and organized into thematic blocks. Accordingly, multivariate modeling provides a natural framework for considering these variables as true groups, thereby accounting for potential dependencies among the responses within each block. In this paper, we propose a multivariate spatial analysis of ordinal survey-based data. This multivariate approach enables the joint analysis of sets of ordinal responses that are likely to be correlated, accounting for individual-level effects, while simultaneously improving the estimation of the geographical patterns for each variable and capturing their interdependencies. We apply this methodology to describe the spatial distribution of several mental health indicators from the Health Survey of the Region of Valencia (Spain) for the year 2022. Specifically, we analyze the block of questions from the 12-item General Health Questionnaire included in the survey.