A multivariate spatial model for ordinal survey-based data

TL;DR

This paper introduces a multivariate spatial modeling approach for analyzing correlated ordinal survey data, improving the understanding of geographical patterns in mental health indicators.

Contribution

It presents a novel multivariate spatial analysis method tailored for ordinal survey responses, capturing dependencies and enhancing spatial pattern estimation.

Findings

Effective joint analysis of mental health survey responses

Improved estimation of geographical distribution of health indicators

Captures interdependencies among survey variables

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.