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Marek - Spatial analyses of health data: From points to models

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Presentation from Third InDOG Doctoral Conference in Olomouc, Czech Republic. 13. - 16. October 2014

Published in: Data & Analytics
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Marek - Spatial analyses of health data: From points to models

  1. 1. Lukáš MAREK Spatial aannaallyysseess ooff hheeaalltthh ddaattaa:: FFrroomm ppooiinnttss ttoo mmooddeellss www.geoinformatics.upol.cz lukas.marek@upol.cz
  2. 2. Why Geographical Information Systems? • Advanced methods for spatial analyses • Spatial statistics • Exploration of spatial pattern • Visualization and presentation for non-geographers (doctors, specialist) www.geoinformatics.upol.cz
  3. 3. Spatial Analyses of Health Data • Disease mapping – Visual description of spatial variability of the disease incidence – Maps of incidence risk, identification of areas with high risk • Geographic correlation studies – Analysis of associations among the incidence and environmental factors • Analyses of spatial pattern – Exploration of spatial and spatio-temporal patterns in data – Disease clusters, randomness, … www.geoinformatics.upol.cz
  4. 4. Objectives • Disease occurence as the event – Space, time, attributes – Spatial (point) pattern • Spatio-temporal evaluation of Campylobacter infection in the Czech Republic • Association with environmental / social factors www.geoinformatics.upol.cz
  5. 5. Tools www.geoinformatics.upol.cz
  6. 6. Campylobacteriosis • Campylobacter spp. (C. jejuni) • Most frequent gastroenteritis in developed countries • Often foodborne • Symptoms are similar to salmonella • Poultry, fresh milk products, sewage, wild animals … www.geoinformatics.upol.cz
  7. 7. Campylobacteriosis • Children are by far the most vulnerable group • Seasonality • Underreported – 225 cases / 100 000 population – Havelaar et al. (2013) • 11.3 times more cases in the Czech Republic • 45.7 times more case in EU www.geoinformatics.upol.cz
  8. 8. Data • EPIDAT database – Mandatory records about infectious diseases and patients, manually fulfilled – Age, Sex, Date, Profession, Place of residence, infection, isolation, … – 2008 - 2012 – ≈ 100 000 records – (weakly) Anonymized • Aggregation – Regular square network, municipal districts • Statistical data – National census 2011 – EUROSTAT population grid www.geoinformatics.upol.cz
  9. 9. Data Privacy • Health and medical data = private, confidential and sensitive data • Keeping all available records but prevent their re-identification • Usefulness of the local scale analysis X privacy protection • Unlikely to explore the relations on the individual level (and not necessary) • Availability, accessibility and restrictions www.geoinformatics.upol.cz
  10. 10. Time www.geoinformatics.upol.cz
  11. 11. Spatial visualization www.geoinformatics.upol.cz
  12. 12. Spatio-temporal visualization www.geoinformatics.upol.cz
  13. 13. Spatio-temporal kriging • Continuous incidence surface in populated places of the Czech Republic • Exploration of the phenomenon‘s autocorrelation simultaneously in space and time • Interpolation of logarithm of standardized incidence into 4 km2 grid • Ordinary global spatio-temporal kriging www.geoinformatics.upol.cz
  14. 14. Spatio-temporal kriging • Exponential model / Metric model • nugget = 0.15, partial sill = 1.94, range = 14150.46 m and space-time anisotropy = 544.58 www.geoinformatics.upol.cz
  15. 15. Spatio-temporal kriging • Vysledky • Bud sada obrazku nebo potom screen video www.geoinformatics.upol.cz
  16. 16. Spatio-temporal clustering • Visual overview of the space-time pattern in Google Earth • Spatial Scan Statistics – Identification of clusters of high and low rate areas together in the continuous geographical areas and time • Age/sex stratified cases, age/sex stratified population, coordinates of centroids • Weekly aggregated data www.geoinformatics.upol.cz
  17. 17. Spatio-temporal clustering • Retrospective analysis based on Poisson probability model • Clusters of maximum size of 3% of population, max. 50% of time or entire time period • Non-parametric temporal adjustment • Monte Carlo simulation, p-value < 0.05 • Comparison based on relative risk www.geoinformatics.upol.cz
  18. 18. Spatial clustering www.geoinformatics.upol.cz
  19. 19. Spatio-temporal clustering www.geoinformatics.upol.cz
  20. 20. Spatio-temporal clustering Cluster Type Time period Region Count Observed Expected Relative risk Population 1* H 2008/01/01 – 2012/12/31 Ostrava 31 5975 2861 2.16 292,978 2 H 2008/01/01 – 2012/12/31 North Wallachia - Lachia 70 5414 2788 2.00 277,236 3 H 2008/01/01 – 2012/12/31 Silesia 16 4773 2534 1.93 256,657 4 H 2008/01/01 – 2012/12/31 Prague - centre 1 1006 245 4.13 29,948 5 H 2008/05/13 – 2010/11/01 South Moravia 167 2274 1432 1.60 292,885 6 H 2008/01/01 – 2012/12/31 Dražíč 1 72 2 41.92 214 7 H 2008/01/01 – 2012/12/31 Brno - centre 19 3951 2590 1.55 271,742 8 H 2008/01/01 – 2012/12/31 Opava 37 1714 877 1.97 87,203 9 H 2008/01/01 – 2012/12/31 Haná 66 3828 2526 1.54 256,721 10 H 2009/04/14 – 2011/09/05 South Wallachia 90 1596 932 1.72 196,522 11 H 2010/01/12 – 2010/02/22 Budweis 60 194 36 5.41 157,425 12 H 2008/01/01 – 2012/12/31 Benešov 15 640 313 2.05 31,115 13 H 2010/04/06 – 2010/10/04 Brno - surroundings 224 568 286 1.99 284,346 www.geoinformatics.upol.cz 14 H 2011/05/03 – 2011/11/14 Pilsen 22 394 201 1.96 197,263
  21. 21. Model • Models help to understand reality – Although they do not have to fully describe its variation • Models help to clarify significance of most likely associated factors • Models help to describe the future using the data from the past • Models help to identify vulnerable areas www.geoinformatics.upol.cz
  22. 22. Arsenault, J. (2010): Épidémiologie spatiale de la campylobactériose au Québec. www.geoinformatics.upol.cz
  23. 23. www.geoinformatics.upol.cz
  24. 24. Conclusions • Space – time aggregation and visualization for visual analytics • Continuous incidence surface describing spatial and temporal progress of the disease • Scan statistics identifying high and low rate clusters as well as their temporal support • Visualization in Google Earth www.geoinformatics.upol.cz
  25. 25. Problems / Challenges • Geocoding • Aggregation • Age / Population standardization • Neighbourhood estimation • Modifiable areal unit problem • Probability distribution of the disease occurrence • Underlying processes • Under / Overestimation of results leading to misinterpretation www.geoinformatics.upol.cz
  26. 26. www.geoinformatics.upol.cz THANK YOU FOR YOUR ATTENTION Lukáš MAREK lukas.marek@upol.cz

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