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