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
Jul 17, 2015
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
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
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
Geography Matters
Significant Data Limitations – Still!Ferri et al, (2005) “Global prevalence of dementia: a Delphi consensus study”, The Lancet
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
ADI Global Consensus RatesSource: Alzheimer ’s Disease International fact sheet 2008
LandScan Population Project
West Africa
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
Basic MethodIvory 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 15565DIX-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 32150VALLEE 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
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
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!
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 ->
Developmental work and resources available at:
www.neura.edu.au/projects/GlobalAD