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Use of Geographical Information
Systems (GIS) in mental health care
Dr Nasser Bagheri, Dr Jose A Salinas
GIS definition
Geographic Information Systems
a GIS is a system of hardware, software and procedures to facilitate the
management, manipulation, analysis, modelling, representation and
display of georeferenced data to solve complex problems regarding
planning and management of resources
(NCGIA, 1990)
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GIS components
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Computer and
peripherals
Procedures and
specifications for the
functioning of GIS
Cartography, DTM,
remote sensing,
Statistics…
Software GIS:
Commercial
Open source
Objectives.
Design,
implementation,
Management and use
Staff
Organisation
Network
Data
Methods
Spatial data models
Territory is splitted in pixels whosegrouping forms spatial objects
Raster model
Continuous data
Graphic Table
GIS Data Type
Spatial objects are simplified in points,
lines and polygons
Vector model
Discrete data
Graphic Table
GIS Data Type
GIS Data visualisation
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Data capture
• Purchase of spatial data
• Capture of spatial data
• Conversion to standard formats
Spatialdatabase
Storage and processing of information
• Storage and organization of geographical data• Spatial relationships (topology, geometry, etc.)• Calculations between variables and link of tables
GIS Data Visualisation
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Analysis and modeling of the information
• Geographical analysis of existing data
• Generation of new information through the transformation or combination the original data
Location. What is there in…?
Questions answered by GIS: Conditions. Where does it happen…?
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Trends. What has changed...? Routes. What is the way to…?
Urban growth in Charleston (USA)Access to Loyola University from the south
GIS Data visualisation
GIS Data Visualisation
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Modeling. What would happen if…? Patterns. What patterns are there…?
GIS Data Visualisation
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Visualisation
• Providing reports and graphic representations of information in digital and analogue formats.
History of using GIS in Health
Hippocrates (4th -5th century): conducted a study of how
location impacts health (Briney)
Dr John Snow (1850s): used hand-drawn maps to show
the locations of cholera deaths in Soho district in
London. He found that the deaths clustered near a
water pump on the city’s Broad Street.
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History of GIS in health services research
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JOHN SNOW’S 1854 CHOLERA MAP
GIS Utilisation in Mental Health
Today GIS is used in mental health and HSR in a number of
different ways;
- In its basic use, GIS answers the question of
“Where?”(Cromley and McLafferty).
- This means questions such as; Where are people living?
Where are hospitals locations? Where are the
clusters/hotspots of mental disorders? Where are mental
health service underutilised or over utilised?
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Privacy and confidentiality of data
Plenty of GIS data available for mental health and HSR
applications. Much of it deals with sensitive information and
as such privacy and confidentiality of individuals is a large
concern among researchers.
However, GIS offers several ways to increase the confidentiality such as;
Geographic attribute masking
Addressing offsets
Using the smaller map scale
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Address geocoding
Patient
ID
Address Postcode
50 20 London St. 2624
51 5 University Avenue 2601
… … …
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Reverse address geocoding
Patient
ID
Address Postcode
50 20 London St. 2624
51 5 University Avenue 2601
… … …
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Geographic masking
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Spatial aggregation greatly reduces the re-identification risk
Reference: Zandbergen A. P. Ensuring Confidentiality of Geocoded Health Data. Advances in Medicine, Volume 2014 (2014)
Geographic masking
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Original locations
Reference: Zandbergen A. P. Ensuring Confidentiality of Geocoded Health Data. Advances in Medicine, Volume 2014 (2014)
Geographic masking
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Original + Masked locations
Reference: Zandbergen A. P. Ensuring Confidentiality of Geocoded Health Data. Advances in Medicine, Volume 2014 (2014)
Geographic masking
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Masked locations
Reference: Zandbergen A. P. Ensuring Confidentiality of Geocoded Health Data. Advances in Medicine, Volume 2014 (2014)
Today…
Modern GIS is used to analyse mental health problems
such as;
Disparity and inequality in mental health care
The availability of mental health care services
Identification of mental disorders clusters
Accessibility to mental health care services
Identification of unmet areas for mental health care
Hot-spots/clusters in mental disorders pattern at
community level21
GIS and mental health policy
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Evidence-informed health policymaking
• Expert opinion
• Research evidence
• Quality of the evidence
• Context
• Global evidence
• Local evidence
Sociodemographic indicators
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• Context analysis
• Risk factors analysisSocial Fragmentation Index
Health Indicators
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• Service utilization (treated, prevalence, incidence, frequency of visits, discharges, readmissions, length of stay,
diagnosis…)
• Mortality (standardized rates, standardized mortality ratios by sex, age, cause)
• Others (health surveys, self-perceived health…)
Organic Senile and presenile mental conditions
Planning service locations
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• Geographical location of mental health services.
• Relationships with indicators
Suitable sites for locating a new pharmacy in
Girona (Spain)
Accessibility to services
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Spatial Data Analysis
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Spatial data analysis gathers a set of techniques to describe and visualize geographycal distributions by
analyzing spatial patterns. It identifies unusual locations, highlights spatial associations, clusters or structures.
Spatial effects
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- Spatial dependence
Does the value of an observation affect the values of the closest observations?
Positive Negative Independence
Spatial effects
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- Spatial heterogeneity
Does the geographical location of the observations affect its values?
Centre/Periphery
High population
North/South
Income
Spatial clusters analysis
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Treated prevalence of depression by municipality
Hot spots and cold spots of treated depression prevalence
Treated prevalence of depression in community mental health centers in Catalonia (2009)
GIS and Mental Health Research
Dementia as an example of GIS application
in mental health research
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Introduction
• Dementia is the second leading
cause of death in Australia
• We have a poor understanding of
whether dementia risk clusters
geographically, how this occurs,
and how dementia may relate to
socio-demographic and built
environment factors
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Aims
1) to estimate the levels of dementia risk in individuals
using general practice data;
2) to assess spatial variation of dementia risk at
community/neighbourhood level; and
3) to identify potential risk clusters (hotspots) and their
association with socioeconomic status
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Methods
• We used 71,413 (14,965 aged 65 and over) active
patients’ records from 16 practices in west Adelaide
• Dementia risk score were calculated using the Australian
National University- Alzheimer’s dementia Risk Index
(ANU-ADRI) tool
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Study area: west Adelaide
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Methods
• Seven risk factors were considered, including age, sex,
body mass index (BMI), blood cholesterol, smoking,
diabetes and depression, and
• Three protective factors including physical activity, social
engagement and alcohol intake.
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Methods
• We aggregated individual dementia risk scores at the
Statistical areas level 1 (SA1).
• SA1 is the smallest area of output for the census of
population and housing and have an average population
about 400 persons.
• Socio-Economic Indexes for Areas (SEFIA) was
extracted from Australian Bureau of Statistics (ABS).
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Australian Statistical
Geography Standard (ASGS)
Structure Diagram
Methods
• We used the Getis-Ord Gi* technique to assess local
spatial cluster of dementia risk.
• A statistically significant large, positive Z-score signifies
a local high-rate cluster (hot spot). Similarly, a
statistically significant large, negative Z-score signifies a
local low-rate cluster (cold spot).
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Results
• Dementia risk scores ranged from -2 to a maximum of 57
points with a median of 26.7 at the individual level
• Dementia risk score was heterogeneous across SA1
with scores ranging from 16.4 to 41.4 (standard deviation
of 4.1)
• The Getis-Ord Gi* analysis showed significant hotspots
in the eastern and southern parts while cold spots were
observed in the western part of Adelaide city.
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Results
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Spatial pattern of dementia
risk at the study area
Results
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Hotspots and coldspots
in dementia risk
Results
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Clusters and outliers (Anselin
Local Moran's I)
Results
• There was negative association between dementia risk
and socioeconomic background of communities (r=-
0.086, p < 0.0001)
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Discussion
• The geospatial analysis of dementia risk at the SA1 level
is the first of its kind using large general practice data
• This finding needs to be explored further to identify
environmental, demographic and lifestyle factors which
seems to offer protection against dementia
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Discussion
• The de-identified general practice data offers potential to
predict dementia risk in the population.
• The geospatial analysis provides a unique approach to
examine spatial pattern of dementia risk across
communities.
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Discussion
Strengths: large sample size and using clinical and
measured risk factors.
Limitations: some protective factors were not recorded in
GP practice data, for example fish consumption
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Conclusion
• To the best of our knowledge, this is the first study to
investigate the spatial heterogeneity of dementia risk in
an urban setting using routinely collected medical data.
• The approach taken in this study will aid policy makers to
target prevention strategies in areas with high dementia
risk to reduce or delay the onset of dementia in
Australian communities.
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Implications of Spatial analysis in MH
• The knowledge on when and where illnesses appear and if their cases are spatially clustered allows us to
state hypothesis on their causes helping to know better their etiology and identify their risk factors.
• Spatial clusters may identify spatial issues such as demographic slowdown, economic imbalance, health
risk or social disruption, which are a priority object of the Administration.
• These studies support the decision-making for the location and allocation of new health resources, the
management of the existing ones, the design of actions for priority illnesses and the programs for
prevention, surveillance and control.
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Questions?
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