A presentation about using spatial science and technology to estimate the global prevalence and impact of the dementias including Alzheimer's disease.

1. The Global Prevalence of Alzheimer's Disease:
An (Introductory) Spatial Analysis
Authors
Hamish Robertson, Doctoral Candidate,
AIHI, UNSW, Sydney, AUSTRALIA
Nick Nicholas, Managing Director,
The Demographer’s Workshop, Sydney, AUSTRALIA

2. Contents
• Introduction
• Modelling prevalence issues under population
ageing/health transition
• Spatial technology, methods and visualisation
• Why his matters and potential utility for
health and social care
• Conclusion
• Future directions

3. Introduction
• Taking a spatial approach to population ageing,
disease expression and systemic responses
• Extending knowledge and planning by modelling
gaps in current understanding
• Using visualisation as a tool for engagement and
potential interventions (clinical, research, policy)
• Improving access to developmental concepts and
methods for a global community of knowledge on
ageing

4. Geography Matters

5. Significant Data Limitations – Still!
Ferri et al, (2005) “Global prevalence of dementia: a Delphi consensus study”, The Lancet

7. Modelling Prevalence
• The dementias in general and AD in particular
• Differential rates including sub-types
• Quality and currency of population data
• Coverage in low resource and/or conflict settings
• Global population and prevalence estimations
• Dynamic variables such as rates by sub-type,
diagnosis, educational levels, economic capacity,
training, workforce, safety in the field etc

8. ADI Global Consensus Rates
Source: Alzheimer ’s Disease International fact sheet 2008

9. LandScan Population Project

10. West Africa

11. Listing for West African Countries
• Benin
• Burkina Faso
• Cape Verde
• Gambia
• Ghana
• Guinea
• Guinea Bissau
• Ivory Coast
• Liberia
• Mali
• Mauritania
• Niger
• Nigeria
• Senegal
• Sierra Leone
• Togo

12. Basic Method
Ivory Coast Latitude Longitude Age 60-64 65-69 70-74 75-79 80+ Total 60+
AGNEBY 5.299999 -4.3372222 14558 10145 6477 3530 2039 36749
BAFING 8.283333 -7.683333 3861 2690 1717 937 540 9745
BAS-SASSANDRA 4.76195 -6.637102 38667 26949 17207 9379 5417 97619
DENGUELE 9.509999 -7.5691667 6166 4297 2743 1495 864 15565
DIX-HUIT
MONTAGNES 7.33333 -7.66667 25960 18090 11549 6296 3635 65530
FROMAGER 6.25 -5.91667 15051 10488 6696 3650 2108 37993
HAUT-SASSANDRA 7 -6.58333 29715 20705 13219 7205 4162 75006
LACS 6.83333 -5.16667 13201 9200 5874 3202 1848 33325
LAGUNES 5.41667 -4.33333 103488 72111 46039 25096 14494 261228
MARAHOUE 7.16667 -5.83333 15379 10716 6842 3730 2154 38821
MOYEN-CAVALLY 6.41667 -7.5 14102 9827 6274 3419 1975 35597
MOYEN-COMOE 6.5 -3.41667 10943 7625 4868 2654 1533 27623
N'ZI-COMOE 7.25 -4.16667 17573 12244 7817 4261 2460 44355
SAVANES 9.5 -5.5 25770 17958 11465 6250 3610 65053
SUD-BANDAMA 5.66667 -5.5 18906 13174 8411 4585 2647 47723
SUD-COMOE 5.5 -3.25 12736 8874 5667 3089 1784 32150
VALLEE DU
BANDAMA 8.25 -4.83333 29952 20872 13324 7263 4195 75606
WORODOUGOU 8.5 -6.33333 10491 7309 4668 2545 1469 26482
ZANZAN 8.5 -3.25 19431 13541 8645 4711 2721 49049

14. Some Limitations
• Some issues with the database i.e. online
system has data gaps (e.g. Niger and Nigeria)
• Prevalence estimate is quite coarse (1.2%) for
more than 250 million people
• Not age or sex-standardised in this version
(but this is feasible and can be upgraded)
• 2011 version of the database (annual release)
• Remaining problem of limited clinical and
population-level research data at this time

15. Conclusion
• Population ageing is multi-scalar: from the global down
to the very local and so too is the epidemiology of
ageing
• Spatial science offers an answer to a variety of issues
including systemic complexity, multiple data sources
and limited data availability
• Neurodegeneration, dementia and sub-type patterns
are likely to be dynamic across geography and over time
(e.g. MCI data, educational levels etc)
• Health concerns are increasingly embedded in highly
dynamic natural and human environmental interactions
e.g. climate change, urbanisation, migration, food
production etc – more changes to come!

16. Future Directions
• Complete data for all of Africa etc
• Finer grained modelling and visualisation e.g.
below provincial administrative level (Admin 1)
• 3 dimensional modelling including urban area
modelling for dynamic cities e.g. Lagos, Accra
• Spatial interpolation to produce topographies of
health conditions such as dementia/AD
• Spatial data mining to identify correlations
between significant or emerging variables
• Scenario modelling to test potential outcomes of
different approaches
• Public access via developmental website ->

17. Developmental work and resources available at:
www.neura.edu.au/projects/GlobalAD