Health Datasets in Spatial Analyses : The General Overview Lukáš MAREK lukas.marek@upol.cz

1. Health Datasets in Spatial Analyses:The General Overview Lukáš MAREK lukas.marek@upol.cz Department of Geoinformatics, Faculty of Science, Palacky University in Olomouc, Czech Republic

2. INTRODUCTION • Advanced methods for spatial analyses • Exploration of spatial pattern • Spatial statistics • Visualization and presentation for non-geographers (doctors, specialist)

3. SPATIAL EPIDEMIOLOGY • Disease mapping • Visual description of spatial variability of the disease incidence • Maps of incidence risk, identification of areas with high risk • Analyses of spatial pattern • Exploration of spatial and spatio-temporal patterns in data • Disease clusters, randomness, … • Geographic correlation studies • Analysis of associations among the incidence and environmental factors

4. HEALTH AND MEDICAL DATA • require specific procedures because of their confidentiality • management, presentation and operations • aggregated, anonymized or incomplete data sets • usage of suitable analytical procedures, while the uncertainty and the inaccuracy of data characteristics need to be taken into account

5. DATA PROVIDERS • International organizations • WHO, EUROSTAT, OECD • INSPIRE directive • Theme Human health and safety (Annex III) • Institute of Health Information and Statistics of the Czech Republic • Czech Statistical Office • National Institute of Public Health

6. DATATYPES • Case-event data • locations of individual cases of a disease, or of individual members of a suitable control group, or covariates. • Irregular lattice data • measures aggregated/averaged to the level of census tracts or other type of administrative district. • Regular lattice data • measures aggregated/averaged to a regular grid (typically arising from remote sensing). • Geostatistical data • measurements sampled at point locations.

7. DATA PRIVACY • Health and medical data = private, confidential and sensitive data • Public health reporting systems and medical registries were committed to the protection of the privacy of the individual • usefulness of the local scale analysis X privacy protection • Availability, accessibility and restrictions

8. SCALE OF THE DATA • Crucial methodological aspect • Addresses or coordinates are the most important information for spatial analyses • But privacy can be easily abused • Unlikely to explore the relations on the individual level (and not necessary) • Mapping to relatively arbitrary administrative areas • Scale sensitive information, MAUP • Different interpretation of findings

9. ANONYMIZATION • spatial and temporal aggregation, • adding geographic or etiologic context variables to original unmasked data and then removing the geographic identifiers, • random small-scale relocation of individual records, • limiting access to potentially identifiable data through a user- and/ or function-restricted computer environment

10. RECORD BASED ANONYMIZATION • Keeping all available records but prevent the re-identification • Weak anonymization • Locations are preserved but other properties are limited so the reconstruction of the individual is limited • Rarely used, outputs for the internal purposes • Randomization • Case locations are preserved but their true positions are moved in certain distance and/or angle • General picture of the spatial data distribution without allowing the identification of individuals

11. SCALE BASED ANONYMIZATION • Aggregation • Most surveillance data are published as summary statistics for administrative level • Areal aggregation vs. Point aggregation • Matching the level of administrative aggregation with the spatial resolution of data • Results obtained from aggregated data should not be used for making assumptions about the nature of an association at the individual level

12. CASE STUDY • Czech Epidemiological Database – EPIDAT • mandatory reporting, recording and analysis of infectious diseases in the Czech Republic • Salmonella cases occurrence in the Olomouc Region in 2002 – 2011 • Aggregation of 11 000 records (in space and/or time)

13. CHOROPLETH MAPS • One of the most common type of map • Added demographic context and irregular lattice aggregation • The data are aggregated to cadastral units and the frequency of the occurrence is re-count to the population • Visual tool for the analysis of spatial distribution of phenomenon • Relative values

14. regular hexagonal grid with the area of average cadastral unit • two kinds of information • the number of salmonella cases per population is expressed by the size of the hexagon, • population in the unit is expressed by the colour

15. QUADTREE MAPS • Quadtree is a recursive algorithm that partitions an area into four initial quadrants and continues to divide each quadrant into four smaller quadrants in a hierarchical way until relatively homogeneous subareas are obtained • Used for the data storage, data aggregation

17. DOT DENSITY MAPS • Usually used for the visualization of any point phenomena • Useful for depicting of the spatial pattern and spatial distribution in the case of aggregated data sets • Dots pattern creates a better visual depiction of the phenomenon in the space • Whether data are combined with the regular or irregular polygon units, the dot density map allows to re-identificate individual cases at least in the certain scale • Dots are usually plotted randomly within boundaries of the areal unit.

19. CONCLUSIONS • The statement about the lack of high-quality health and medical data sets is not fully true • The question should not be only about the existence of data, but about their availability and the accessibility as well as about restrictions regarding to their usability and the usefulness of outputting results • Results obtained from aggregate data should not be used for making assumptions about the nature of an association at the individual level

20. ACKNOWLEDGEMENT The author gratefully acknowledge the support by the Operational Program Education for Competitiveness - European Social Fund (project CZ.1.07/2.3.00/20.0170 of the Ministry of Education, Youth and Sports of the Czech Republic)

21. THANK YOU FOR YOUR ATTENTION Health Datasets in Spatial Analyses: The General Overview Lukáš MAREK lukas.marek@upol.cz Department of Geoinformatics, Faculty of Science, Palacky University in Olomouc, Czech Republic