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Adding geographical detail to social surveys:
Estimating local disability prevalence
Alan MarshallESDS Government
15th April 2010
The problem
• Researchers require detailed local information, for example, to provide appropriate services. BUT:
• The data that is available for small areas (districts, wards) often lacks detail– Census question on limiting long term illness
and disability
• Detailed data sources lack geography (confidentiality) – HSE has information on specific disabilities but
does not distinguish district of residence
• Combining locally available data with survey data offers a solution to this problem
Disability age pattern
0.1
.2.3
.4.5
0 20 40 60 80age
Observed rates Model rates
Disability rates (Higher severity) by age in England (females)
Source: Health Survey for England 2001
Many disability types are strongly linked to age
Prevalence ratio method
• Multiply the national disability rate (HSE) and the local population count at each age.
• This approach is used by the POPPI and PANSI websites to estimate mobility and personal care disability.
• Developed by the Institute for Public Care.
• Designed to help explore the possible impact that demography and certain conditions may have on populations.
• http://www.pansi.org.uk/• http://www.poppi.org.uk/
3000 2000 1000 0 1000 2000 3000 4000 5000
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88A
ge
Population
Bolton population pyramid – 2001 and 2021
Males Females
Grey bars indicate the population in 2001Clear bars indicate the population in 2021 Source: ONS MYE and pop projections
10000 8000 6000 4000 2000 0 2000 4000 6000 8000
0
5
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Population
Manchester population pyramid – 2001 and 2021
Males Females
Grey bars indicate the population in 2001Clear bars indicate the population in 2021 Source: ONS - MYE and pop projections
Including further local information
• Disability is linked to characteristics other than age that are available for local areas in the census
• E.g. LLTI increases the risk of having a personal care disability
If two districts with same age structure But one has higher level of LLTIThen we would expect higher levels of personal care disability
Relational models
0.2
.4.6
.8P
reva
len
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20 40 60 80 100Age
LLTI - Census Personal care - HSEPersonal care - Modelled
England - MalesLLTI and Personal care disability prevalence
Two adjustments relate the LLTI curve to the PC curve.
Comparing PANSI and relational estimates of personal care disability
Personal care prevalence (2008) (18-64)
SIR PANSI
Relational estimate
South Bucks 0.66 5.0 2.4 Brighton 0.98 4.1 4.3 Bury 1.05 4.7 5.2 Pendle 1.16 4.8 6.1 Wakefield 1.22 4.8 6.3 Easington 1.63 4.8 9.9
SIR = Standardised illness ratio
Other approaches – Area classifications
• People with similar socio-demographic characteristics cluster together within certain areas
• Classify areas into groups according to the socio-demographic characteristics of their populations
• ACORN MOSAIC – commercial classifications
• ONS classifications (since 1970s)
• HSE (2001) includes ONS area classification and urban/rural classifications
ONS Area classifications
ONS district classification (2001) SIRs – Census 2001SIR (2001)
Evaluating local estimates
• Catch 22 – if there were local estimates then we wouldn’t need to create them!
• Compare with proxy data - administrative records
• Seek opinions of local experts
• Compare with locally conducted surveys
• Compare estimates produced using different methods
Disability estimates from mythesis will soon be at:
http://www.ccsr.ac.uk/staff/am.htm
POPPI and PANSI websites:
http://www.pansi.org.uk/http://www.poppi.org.uk/
Output area classification group
http://areaclassification.org.uk/
Bajekal, M., Scholes, S.,Pickering, K. and Purdon, S.(2004). Synthetic estimation ofhealthy lifestyle indicators: Stage1 report. NatCen, London
Skinner, C. (1993). The Use ofSynthetic Estimation Techniquesto Produce Small AreaEstimates. New MethodologySeries NM18. OPCS. London.
Resources References
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