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

Lukáš MAREK
Spatial aannaallyysseess ooff hheeaalltthh ddaattaa::
FFrroomm ppooiinnttss ttoo mmooddeellss
www.geoinformatics.upol.cz
lukas.marek@upol.cz

Why Geographical Information Systems?
• Advanced methods for spatial analyses
• Spatial statistics
• Exploration of spatial pattern
• Visualization and presentation for non-geographers
(doctors, specialist)

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, …

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

Tools

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
…

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

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

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

Time

Spatial visualization

Spatio-temporal visualization

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

• Exponential model / Metric model
• nugget = 0.15, partial sill = 1.94, range = 14150.46 m
and space-time anisotropy = 544.58

• Vysledky
• Bud sada obrazku nebo potom screen video

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

• 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

Spatial 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
14 H 2011/05/03 – 2011/11/14 Pilsen 22 394 201 1.96 197,263

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

Arsenault, J. (2010): Épidémiologie spatiale de la campylobactériose au Québec.

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

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

THANK YOU
FOR
YOUR ATTENTION
Lukáš MAREK
lukas.marek@upol.cz

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

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