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Interpolation between spatial frameworks: an application of process convolution to estimating neighbourhood disease
1Department of Geography and Centre for Statistics, Queen Mary University of London, UK.
Statistical Methods in Medical Research
|May 5, 2012
Summary
This study develops a method to estimate disease prevalence in small areas using data from larger health service areas. It enables spatially interpolated estimates for neighbourhoods, improving public health insights.
Area of Science:
- Spatial epidemiology
- Geographic health data analysis
- Public health surveillance
Background:
- Health data is often collected within administrative boundaries (e.g., general practitioner practices) but policy-relevant health contrasts exist at finer spatial scales (e.g., neighbourhoods).
- Current UK data provides disease prevalence for general practitioner practices but lacks neighbourhood-level estimates, hindering localized public health interventions.
- Bridging this data gap is crucial for understanding and addressing spatial health inequalities.
Purpose of the Study:
- To develop and validate a statistical method for spatially interpolating disease prevalence estimates from one spatial framework (general practitioner practices) to another (neighbourhoods).
- To incorporate neighbourhood indicators as reflective measures of latent disease prevalence.
- To apply the method to estimate psychosis prevalence in northeast London.
Main Methods:
- Application of discrete process convolution for spatial interpolation between area units.
- Modification of the interpolation to include neighbourhood indicators (e.g., hospitalisation rates) as reflective constructs of latent disease prevalence.
- Utilisation of a zero-inflated Poisson model as the likelihood for reflective indicators, demonstrated with psychosis prevalence data.
Main Results:
- The discrete process convolution effectively provides spatially interpolated disease prevalence estimates for neighbourhoods from general practitioner practice data.
- Incorporating neighbourhood indicators improved the estimation of latent neighbourhood disease prevalence.
- Sensitivity analysis assessed the impact of kernel choice (e.g., normal vs. exponential) on the interpolation results.
Conclusions:
- The developed method successfully bridges spatial data gaps, enabling neighbourhood-level disease prevalence estimation.
- This approach enhances the utility of existing health data for localized public health policy and planning.
- The methodology offers a flexible framework for spatial health data interpolation and analysis.
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