adding geographical detail to social surveys: estimating local disability prevalence alan marshall...
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Adding geographical detail to social surveys:
Estimating local disability prevalence
Alan MarshallESDS Government
15th April 2010
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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
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Disability age pattern
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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
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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/
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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
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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
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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
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Relational models
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LLTI - Census Personal care - HSEPersonal care - Modelled
England - MalesLLTI and Personal care disability prevalence
Two adjustments relate the LLTI curve to the PC curve.
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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
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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
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ONS Area classifications
ONS district classification (2001) SIRs – Census 2001SIR (2001)
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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
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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