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The spatial structure of chronic morbidity: evidence from UK census returns
Peter F Dutey-Magni1,2, Graham Moon3
1Geography and Environment, University of Southampton, University Road, Southampton, SO17 1BJ, UK. p.dutey-magni@soton.ac.uk.
International Journal of Health Geographics
|August 26, 2016
Summary
Understanding spatial dependency in chronic morbidity is crucial for accurate disease mapping. This study found that migration patterns significantly influence spatial autocorrelation, improving disease prevalence predictions when modeled.
Area of Science:
- Epidemiology
- Geographic Information Systems (GIS)
- Statistical Modeling
Background:
- Disease prevalence models are vital for estimating health characteristics in small geographical areas lacking data.
- Decisions on spatial assumptions within these models, particularly covariance structure, require further understanding.
- This research focuses on spatial dependency processes in unexplained chronic morbidity variation.
Purpose of the Study:
- To investigate spatial dependency in chronic morbidity across England and Wales using UK census data.
- To evaluate different spatial structures, including distance, contiguity, and migration flows, for modeling spatial dependency.
- To assess the impact of spatial autocorrelation on disease prevalence predictions.
Main Methods:
- Utilized 2011 UK census data on limiting long-term illness (LLTI) across Local Authority Districts (LADs) and Middle Layer Super Output Areas (MSOAs).
- Measured variance and spatial clustering of LLTI odds across demographic cross-classifications.
- Employed logistic mixed models with various spatial weights matrices (distance, contiguity, migration) to analyze LLTI spatial structure and covariate associations.
Main Results:
- Chronic illness odds showed greater dispersion than expected from demographic factors alone.
- The three-nearest neighbour method best represented spatial structure among tested adjacency matrices.
- Migration flow-based spatial weights matrices revealed significant spatial autocorrelation in LLTI.
- Substantial spatial autocorrelation persisted after accounting for LAD-level covariates, improving prevalence predictions when modeled.
Conclusions:
- Systematic analysis of spatial dependency is essential for advancing chronic disease mapping tools.
- Migration-based spatial structures effectively capture LLTI autocorrelation and are practical to develop.
- Spatial dependency patterns in LLTI vary across ethnic groups, necessitating ethnic stratification of health data.
- Improving access to disaggregated data can enhance the complexity and accuracy of prevalence models.
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