Globalising the Study and Analysis of Alzheimer’s Disease: A Digital Earth Model

AuthorsHamish Robertson*, Doctoral Student, AIHI, University of NSW, AUSTRALIA

Nick Nicholas, Managing Director, The Demographer’s Workshop, Sydney

Joanne Travaglia, PhD, Faculty of Medicine, UNSW

Associate Professor Andrew Georgiou, AIHI, UNSW

Associate Professor Julie Johnson, AIHI, UNSW

Contents• Introduction

• Dementia and AD prevalence under population ageing and health transition

• Environmental change and urban growth

• Spatial technology, methods and visualisation

• Model to date

• Potential applications

• Conclusion

• Future directions

Introduction

• Taking a spatial approach to population ageing, disease expression and potential systemic responses

• Developing and extending our knowledge and options by modelling

• Using spatial simulation and visualisation as tools for engagement and potential interventions (clinical, research, policy and practice)

• Improving access to developmental concepts and methods for a global community of knowledge on ageing

• Supporting older people, advocates and service providers

Geography Matters

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

Issues with Modelling Prevalence

• The dementias in general and AD in particular• Differential rates including sub-types vary by

location• 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

• Population-level knowledge versus clinical studies versus informed estimates

ADI Global Consensus RatesSource: Alzheimer ’s Disease International Fact Sheet 2008

LandScan Population Project

West Africa

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

Dementia in West Africa

Current Limitations• Changes in administrative boundaries a common occurrence

• Changes in political boundaries less common but still happen e.g. South Sudan

• Changes in names/naming systems/language use and transliteration into English

• Some issues with the database i.e. online system has some data gaps in oldest age cohorts in some countries

• Prevalence estimate is still somewhat coarse for a billion plus people and population growing

• 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 a potential 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!

Future Directions

• Complete data, error checking and scale issues

• 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