Small area spatial modelling and mapping of health
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The document discusses small area spatial modeling and mapping of health, focusing on health inequalities and the use of ArcGIS for measuring demand and access to services. It outlines methods for linking datasets to estimate health outcomes and highlights challenges in applying such models in low- and middle-income countries. Key references include studies on diabetes risk modeling and spatial microsimulation for health behaviors.
Small area spatial modelling and mapping of health
- 1. Small area spatial modelling & mapping of health Dianna Smith 11 March 2013
- 2. Research streams Social and spatial health inequalities in US, UK, NZ Measuring demand/access to services: ArcGIS Modelling demand/access to services: spatial interaction Filling in missing data: spatial microsimulation for synthetic populations
- 3. Mathur, R., Noble, D., Smith, D., Greenhalgh, T., & Robson, J. (2012). Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross-sectional analysis. British Journal of General Practice, 62(603), e663-e670. © The authors
- 4. Spatial interaction (gravity) m m m Identify the relative m S ij Oi Ai W j exp( d ij ) m attractiveness of a destination for segments of the population Calculate demand for services Or location-allocation for mobile services
- 6. Linking datasets and building estimates If there is a Health data Census representative (non-spatial) (spatial) population survey that age age has variables in sex sex common with a ethnicity census, these variables ethnicity can be used to calculate social grade social grade the probabilities of each BMI (obesity) person in the survey diabetes living in each area. If the survey includes data on obesity and diabetes for each person, every time a person is „assigned‟ to an area the prevalence
- 7. A good model The linking characteristics must be predictive of the health outcome for that population In Scotland, ethnicity is irrelevant for BMI estimation Helps if there is some scale where prevalence is known Aggregate the estimated data to check accuracy Cluster the areas with populations which are most similar in terms of predictive characteristics for the behaviour/outcome Global smoothing algorithm can create error in „unusual‟ populations
- 8. Area characteristics: clusters Cluster membership 1:High % in DE, over 50 2: Low % DE, over 50 3: Low % DE, high % over 50 ± 4: High % non-white, % DE, % under 50 5: High % DE, % under 50 0 3 6 12 Kilometers
- 9. Estimated Diabetes LS29 LS21 BD20 LS19 LS20 BD21 LS19 BD16 LS17 BD17 LS16 LS18 BD10 BD22 BD18 LS6 BD15 LS5 BD9 BD2 LS13 LS4 BD8 LS3 LS28 BD3 BD13 BD1 LS12 BD7 BD14 BD5 LS11 Diabetes SIR BD4 (Optimal model) BD6 75.5 - 92.8 BD11 LS27 92.9 - 100.0 ± BD12 100.1 - 150.0 150.1 - 250.0 BD19 0 1 2 4 6 8 250.1 - 350.1 Kilometers
- 10. Potential applications Building on incomplete datasets from current records: linking data from multiple sources (building on Tatem et al 2012, “Mapping populations at risk” Estimating outcomes of interventions: scenario modelling
- 11. Difficulties All very quantitative models Minimal applications (of the models) in LMICs, especially in terms of health outcomes
- 12. References Mathur, R., Noble, D., Smith, D., Greenhalgh, T., & Robson, J. (2012). Quantifying the risk of type 2 diabetes in East London using the QDScore: a cross- sectional analysis. British Journal of General Practice, 62(603), e663-e670. Smith, D. M., Pearce, J. R., & Harland, K. (2011). Can a deterministic spatial microsimulation model provide reliable small-area estimates of health behaviours? An example of smoking prevalence in New Zealand. Health & Place, 17(2), 618-624. Tatem, A., Adamo, S., Bharti, N., Burgert, C., Castro, M., Dorelien, A., et al. (2012). Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation. Population Health Metrics, 10(1), 8.