Adding geographical detail to social surveys: Estimating local disability prevalence Alan Marshall ESDS Government 15 th 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 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.

Bolton population pyramid – 2001 and 2021 MalesFemales Grey bars indicate the population in 2001 Clear bars indicate the population in 2021 Source: ONS MYE and pop projections

Manchester population pyramid – 2001 and 2021 Males Females Grey bars indicate the population in 2001 Clear 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 LLTI Then we would expect higher levels of personal care disability

Relational models Two adjustments relate the LLTI curve to the PC curve.

Comparing PANSI and relational estimates of personal care disability 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 district classification (2001)SIRs – Census 2001

Evaluating local estimates Catch 22 – if there were local estimates then we wouldnt 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 my thesis will soon be at: POPPI and PANSI websites: Output area classification group Bajekal, M., Scholes, S., Pickering, K. and Purdon, S. (2004). Synthetic estimation of healthy lifestyle indicators: Stage 1 report. NatCen, London Skinner, C. (1993). The Use of Synthetic Estimation Techniques to Produce Small Area Estimates. New Methodology Series NM18. OPCS. London. ResourcesReferences