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Mapping socio-economic status using mixed data: a hierarchical Bayesian approach.

Gabrielle Virgili-Gervais1, Alexandra M Schmidt1, Honor Bixby2

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian hierarchical model for estimating socio-economic status (SES) using mixed data. The model effectively identifies key indicators like housing density and sanitation for differentiating SES levels in Ghana.

Keywords:
Bayesian hierarchical modellingconditional auto-regressive modelsfactor analysisgreater Accra metropolitan areasocio-economic status

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Area of Science:

  • Statistics and Econometrics
  • Socio-economic Modeling
  • Spatial Analysis

Background:

  • Existing socio-economic status (SES) models often require data aggregation, limiting their ability to capture local variability.
  • There is a need for advanced statistical models that can handle mixed data types (dichotomous and continuous) and incorporate spatial structures for SES estimation.
  • Previous factor analysis models by Quinn (2004) and Schliep and Hoeting (2013) provide a foundation for mixed-response data but lack spatial hierarchy.

Purpose of the Study:

  • To propose a novel Bayesian hierarchical model for estimating a socio-economic status (SES) index using mixed dichotomous and continuous variables.
  • To extend existing factor analysis approaches by incorporating a spatial hierarchical structure for key model parameters.
  • To enable the direct use of household-level census data without prior aggregation, thereby better accommodating spatial SES variability.

Main Methods:

  • Development of a Bayesian hierarchical model building upon factor analysis frameworks for mixed data.
  • Inclusion of a spatial hierarchical structure to model parameter variations across geographical areas.
  • Application of the model to 10% of the 2010 Ghana census data for the Greater Accra Metropolitan area, utilizing 20 observed variables.

Main Results:

  • The proposed hierarchical model effectively estimates a socio-economic index using household-level data.
  • Key variables such as the number of people per room, access to water piping, and flushable toilets were identified as strong differentiators of high and low SES areas.
  • The model successfully accommodated the variability of SES between census tracts and the number of households per area.

Conclusions:

  • The Bayesian hierarchical model offers a robust approach for SES index estimation with mixed data and spatial dependencies.
  • The model's ability to use household-level data directly enhances the accuracy and granularity of SES assessments.
  • The findings highlight the importance of housing conditions and sanitation in defining socio-economic disparities in the studied region.