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Bayesian modeling of spatial ordinal data from health surveys
Miguel Ángel Beltrán-Sánchez1, Miguel-Angel Martinez-Beneito1, Ana Corberán-Vallet1
1Department of Statistics and Operations Research, University of Valencia, Burjassot (Valencia), Spain.
Statistics in Medicine
|July 18, 2024
Summary
This study introduces a Bayesian model for estimating health indicators from surveys, particularly for small areas. The method accurately maps self-perceived health across regions, aiding public health insights.
Area of Science:
- Public Health
- Biostatistics
- Spatial Analysis
Background:
- Health surveys provide crucial data on health indicators not available in routine registries.
- Ordinal variables and individual covariates are common in health survey data.
- Small-area estimation is vital for understanding localized public health trends.
Purpose of the Study:
- To propose a Bayesian individual-level model for small-area estimation of survey-based health indicators.
- To incorporate ordinal data and spatial dependencies into the model.
- To enable extrapolation of results to various administrative divisions, including small areas.
Main Methods:
- A Bayesian individual-level model utilizing a categorical likelihood for ordinal data.
- Incorporation of spatial dependence using a conditional autoregressive distribution.
- Application of post-stratification for extrapolating estimates to different areal units.
Main Results:
- The methodology was successfully applied to estimate a self-perceived health indicator.
- The geographical distribution of self-perceived health in the Valencia Region (2016) was described.
- The model demonstrated effective small-area estimation for survey-based health indicators.
Conclusions:
- The proposed Bayesian model is effective for small-area estimation of ordinal health indicators.
- The method allows for detailed geographical mapping of health data.
- This approach enhances public health surveillance and planning by providing localized health insights.
Keywords:
individual‐level modelordinal data analysispost‐stratificationspatial statisticssurvey‐based studiesMore Related Videos
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