Disability Futures: A Spatial Perspective on the Future of Disability in NSW
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Transcript of Disability Futures: A Spatial Perspective on the Future of Disability in NSW
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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
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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
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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
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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!
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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
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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).
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Population Structure and Ageing
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Geography and Disability
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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!
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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)
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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
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Older People with a Disability as a % of PopulationChange from 2006 to 2031
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People with a Disability as a % of PopulationChange from 2006 to 2031
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People with a Disability as a % of PopulationChange from 2006 to 2031
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Geography: SLA’s
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Statistical Divisions
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Public Hospitals in NSW
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People Aged 85 + in Western Sydney CCD Level
Western Suburbs:
People Aged 85+
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Forecast Alzheimer’s Disease Prevalence by 2031
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Mapping a Disability Client Group
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Moving average of client group distribution
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Relationship of Clients to Home Visit Staff
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Distance to Home Visit Staff
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Housing Providers and Service Planning Example
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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
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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