1. National Institute for Public Health and the Environment The Dutch RIF Experiences and possibilities

2. National Institute for Public Health and the Environment Introduction RIF implementation part of Smarhagt project Development of toolkit for small area health analysis Comparison with more complex methods -using R, Winbugs Cluster analysis

3. National Institute for Public Health and the Environment Practical issues RIVM software standards versus RIF requirements First test: Scottish Lip Cancer data set Second test: existing RIVM data set -hospital admissions (icd, 2001-2004) -postal code areas (surrounding Schiphol Airport) -age, sex, SES, ethnicity -aircraft noise exposure

4. National Institute for Public Health and the Environment Risk Analysis in RIF Focuses on relation between exposure and RR Exposure to be available as: -Location point source -Dispersion map -Exposure levels per small area (which we use) Maximum 7 exposure categories Test for heterogeneity and linear trend Graphs and spreadsheet of RR by exposure GIS required to interpret location/dispersion from map

5. National Institute for Public Health and the Environment Risk Analysis Exposure Data

6. National Institute for Public Health and the Environment Risk Analysis Result

7. National Institute for Public Health and the Environment Disease Mapping in RIF Map based on number of cases per PC4 area Number of residents per PC4 area differs Resulting in a difference in precision of the numbers ‘1 out of 10’ is less precise than ‘100 out of 1000’ This causes spurious outliers on the map Solution: smoothing -> see next sheet In which imprecision determines severity

8. National Institute for Public Health and the Environment Smoothing in RIF Calculates mean number of cases in whole study area From which follows expected number of cases per PC4 area Compares this to actual number of cases in PC4 area Adjusts differences according to numbers of residents: -many residents – smaller adjustment -few residents – bigger adjustment Smoothing based purely on statistical grounds Spatial patterns in disease may be lost in smoothing! ‘Empirical Bayes model’

9. National Institute for Public Health and the Environment Smoothing from RIF in Winbugs Uses information from neighbouring PC4 areas To calculate local average number of cases for PC4 area Also gives an expected number of cases for PC4 area Compares to the actual number of cases in PC4 area Also adjusts differences according to number of residents This is spatial smoothing Spatial patterns in disease are maintained! ‘Fully Bayes model’ (Besag-York-Mollië)

10. National Institute for Public Health and the Environment Disease Mapping Result

11. National Institute for Public Health and the Environment Disease Mapping Result

12. National Institute for Public Health and the Environment Disease Mapping Result

13. National Institute for Public Health and the Environment Disease Mapping Result

14. National Institute for Public Health and the Environment Disease Mapping Result

15. National Institute for Public Health and the Environment Disease Mapping Result

16. National Institute for Public Health and the Environment Environmental Data Health Registration Data Geographical Data Population Data Covariate Data (f.i. SES) Risk AnalysisDisease Mapping RIF Rapid Inquiry Facility

17. National Institute for Public Health and the Environment Data obstacles Postal code areas vary, no useful hierarchy Privacy rules limit resolution for health data Area size limits -Accuracy of exposure data -Usefulness of point source data Use of modified ICD9 for Dutch registry Data set size limit of 2 Gb Consensus on / availability of exposure maps

18. National Institute for Public Health and the Environment Possible future of Dutch RIF Extend database to national coverage Monitoring through yearly updates Depends on availability of relevant disease data Depends on availability / development of exposure data … …