This document describes a data-driven model for visualizing and projecting dementia cases in Australia over time. The model integrates population data, dementia prevalence estimates, subtype data, severity estimates, and cost of care estimates. It allows users to visualize dementia projections and costs at the national, state, and local geographic level. The model is meant to help service providers plan for future demand, identify areas of higher need, and conduct financial modeling to improve outcomes and allocate resources efficiently.

1. Visualising
the
Dementias in
Australia:
A Data Driven
Model
• Authors:
• Hamish Robertson
• Nick Nicholas
• University of Technology Sydney

2. Contents
• The dementias in Australia
• Continuing data problems
• Model
• Visualisation
• Data integration
• Applications
• Conclusion

3. The Dementias in Australia
• Mid-tier OECD population ageing, peaking in approximately 2050
• Dementia a broad rubric with many single conditions and growing
overlap e.g. mixed dementia category
• Dementia landscape is dynamic, hence modelling flexibility is useful,
worthwhile and increasingly viable
• Interaction between funding, policy, population and things like Royal
Commissions make managing change more important in modelling
and response activities
• Expansion and extensibility therefore are key considerations

4. Continuing
Data
Problems

5. Model
• Census data at SA2 level
• AIHW population projections to 2032 at SA2 level
• Dementia prevalence estimates 2017-2032
• Sub-type prevalence (AD, Vascular, Other) – base data and slider
option
• Severity estimates included to allow for more complex needs
identification and planning
• Frailty measures – base data and slider option being added
• Per capita formal cost of care estimates in development

6. Visualisation

9. Dementia Time Series Data

10. Time Series for Severity Estimates

11. Data Structure

12. Data Integration
• Many different data sets included within the one model
• Connected at the geographic level so geography provides the
integrative dimension linking all data types
• Readily extensible and expandable – new and additional data sets
from different parts of the ‘system’ can be integrated within the same
modelling environment
• Scalable – from national level, state and territory level, down to small
area (SA2 here but S1, meshblock etc. all viable)

13. Applications
• Consistent data modelling and scenario development across service
providers e.g. analyse against service provider types, model emerging
demand, identify areas of higher need versus lower (quantitively)
• Easily adjustable based on new or additional data (e.g. new
prevalence data from research and/or administrative data sets)
• Financial modelling extensibility – per capita cost, area-based costing
or system and sub-system costings (e.g. all of acute care or
components of acute care, all of residential care or networks of
providers or individual providers)
• Add in a severity measure to connect demand, cost and resourcing
sensitivity

14. Conclusion
• More and better data will expand and improve the detail of the
dementia ‘picture’ in Australia e.g. below state and territory level
• Dynamic nature of the dementias (mixed dementia etc.) and their
interaction with a shifting demographic profile e.g. differential growth
in older cohorts
• Data viz and spatial applications can assist multiple providers in
tailoring responses, conserving resources and improving outcomes
• The same approaches can be used to support and develop
community understanding and participation in building an age-
friendly society e.g. volunteers and carers