Disability Futures:

A Spatial Perspective on the

Future of Disability in NSW

Hamish Robertson, Ageing Research Centre, Prince of Wales Hospital

Nick Nicholas, Ageing Research Centre, Prince of Wales Hospital

GA (Tony) Broe, Prince of Wales Medical Research Institute

Contents

• Introduction

• Population ageing and disability

• Ageing, disability and location

• Modelling current and future disability• Modelling current and future disability

• Locating disabilities and their social and service implications

• Spatialising the future of disability

• Summary

• Conclusion

Ageing, disability and location

• Population ageing is not uniformly distributed in Australia – SA, Tas, Vic, NSW etc

• Disability needs to be seen as evolving • Disability needs to be seen as evolving and not simply a static (disease) category

• Cognitive disability in later life is going to emerge as a significant issue

• Frailty concept – more related to co-morbidity than disability

Population Ageing and Disability

• Current population ageing will not peak until at least 2050

• We will have the oldest population we have ever had

• It will have significant social effects because it is • It will have significant social effects because it is (creating) a form of social change

• This will have implications for the full spectrum of social policy and related providers including all health and disability services – eg. carers, donations, costs etc

• Neither a crisis nor a surprise!

Australian Population DynamicsMedium Level ABS Forecasts 2006-2031

6.00

7.00

8.00

9.00

Projected Australian Population

Aged 65 years and over

2006 2011 2016 2021 2026 2031 2036 2041 2046 2051

0.00

1.00

2.00

3.00

4.00

5.00

Year

Millio

ns

Population of older persons by state and territory in AustraliaState % of resident population over 65 years

2002 2021 2051

New South Wales 13.2 19.0 26.9

Victoria 13.1 19.1 27.3

Queensland 11.8 18.5 26.8 Queensland 11.8 18.5 26.8

South Australia 14.8 22.2 31.1

Western Australia 11.2 18.4 26.9

Tasmania 14.0 23.4 33.8

Northern Territory 3.9 8.1 12.1

Australian Capital Territory 8.8 16.4 23.5

Source: Population Projections, Australia, 2002-2101(ABS Cat No.3222.0).

Population Structure and Ageing

Geography and Disability

Concept Design

Population Model

Disability Prevalence

Model

Calculateprojected

prevalence by year

Geography Selection- SLA’s

Calculateprevalence

for each location

Identify areas of

significant change

Add extra

geographies

Compute effects

by location

Present startling results at

conference!

Pilot Model of Disability: Age and Sex-Specific Cohorts

• Based on ABS disability rates for NSW 2003

• No distinctions in this model for severity (can be added)

• Scale quite coarse – SLA at this stage (eg. CCD, mesh block)

• Life expectancies are still rising = longer life, probably with disability

Sex

Age Rate %

65-69 70-74 75-79 80-84 85+

Male 34.6 48.2

(+13.6%)

56.4

(+8.2)

63.1

(+6.7)

79.4

(+16.3)

Female 38.4 46.0

(+7.6)

55.3

(+9.3)

73.5

(+18.2)

77.0

(+3.5)

Projections of People Aged 65 Plus with Disability 2001 to 2031 (Single rate)

600000

800000

1000000

1200000

0

200000

400000

600000

Year2

001

Year2

006

Year2

011

Year2

016

Year2

021

Year2

026

Year2

031

Older People with a Disability as a % of PopulationChange from 2006 to 2031

People with a Disability as a % of PopulationChange from 2006 to 2031

People with a Disability as a % of PopulationChange from 2006 to 2031

Geography: SLA’s

Statistical Divisions

Public Hospitals in NSW

People Aged 85 + in Western Sydney CCD Level

Western Suburbs:

People Aged 85+

Forecast Alzheimer’s Disease Prevalence by 2031

Mapping a Disability Client Group

Moving average of client group distribution

Relationship of Clients to Home Visit Staff

Distance to Home Visit Staff

Housing Providers and Service Planning Example

Summary

• Age and gender specific rates of disability plus population ageing will contribute to significant regional effects and intra-urban effects

• Regional areas, in this analysis, are likely to see greatest effects/impactsgreatest effects/impacts

• Changes in ageing/disability interactions are likely to change this picture (+/-?)

• Geography of disability is going to be spatially dynamic in ways that need to be thought through

• Spatial science has a contribution to make to silo-based systems

Conclusion

• Limitations – all forecasts are problematic, prevalence rate we used is crude and true surface mapping not available in this software

• Developments – add finer gradations of prevalence, finer/more complex geographies (eg. CCD), severity factors, service infrastructure, more actual data factors, service infrastructure, more actual data (incidence, costs etc)

• Additional techniques – spatial statistical analysis once extra data etc added (data mining), surface mapping

• Implications – a genuine geography of disability as a developmental and dynamic socio-demographic process