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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