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The utility of geodemographic indicators in small area estimates of limiting long-term illness

The utility of geodemographic indicators in small area estimates of limiting long-term illness
The utility of geodemographic indicators in small area estimates of limiting long-term illness

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as ‘blue collar’ and ‘struggling families’ being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.

Geodemographics, Limiting long term illness, Multilevel modelling, Small area estimation
0277-9536
Moon, Graham
68cffc4d-72c1-41e9-b1fa-1570c5f3a0b4
Twigg, Liz
41a8c6df-488f-4c0f-b38d-e83b8b41728c
Jones, Kelvyn
0df468d5-eeec-4694-8415-352df0e2e36e
Aitken, Grant
c98d55a9-eeb7-4e1b-bc5e-fdb2e47a545f
Taylor, Joanna
40b1395b-e282-4efa-9e4e-cb994987a496
Moon, Graham
68cffc4d-72c1-41e9-b1fa-1570c5f3a0b4
Twigg, Liz
41a8c6df-488f-4c0f-b38d-e83b8b41728c
Jones, Kelvyn
0df468d5-eeec-4694-8415-352df0e2e36e
Aitken, Grant
c98d55a9-eeb7-4e1b-bc5e-fdb2e47a545f
Taylor, Joanna
40b1395b-e282-4efa-9e4e-cb994987a496

Moon, Graham, Twigg, Liz, Jones, Kelvyn, Aitken, Grant and Taylor, Joanna (2018) The utility of geodemographic indicators in small area estimates of limiting long-term illness. Social Science & Medicine. (doi:10.1016/j.socscimed.2018.06.029).

Record type: Article

Abstract

Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as ‘blue collar’ and ‘struggling families’ being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status.

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Accepted/In Press date: 23 June 2018
e-pub ahead of print date: 26 June 2018
Keywords: Geodemographics, Limiting long term illness, Multilevel modelling, Small area estimation

Identifiers

Local EPrints ID: 422693
URI: https://eprints.soton.ac.uk/id/eprint/422693
ISSN: 0277-9536
PURE UUID: 2103c4cc-be1d-4535-ba9b-33b299adc119
ORCID for Graham Moon: ORCID iD orcid.org/0000-0002-7256-8397

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Date deposited: 31 Jul 2018 16:30
Last modified: 14 Mar 2019 01:41

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Contributors

Author: Graham Moon ORCID iD
Author: Liz Twigg
Author: Kelvyn Jones
Author: Grant Aitken
Author: Joanna Taylor

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