Creating the UK National Statistics 2001 Output Area Classification

Vickers, Daniel; Rees, Phil

doi:10.1111/j.1467-985x.2007.00466.x

Cited by 169 publications

(166 citation statements)

References 10 publications

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“…Geo-demographic classifications have sought to categorise areas in terms of the types of individuals that characterise them (Vickers & Rees 2007). Their popularity has seen the field develop into a multi-million pound industry with their resulting software tools being used in many commercial and public sector organisations (Harris et al 2005).…”

Section: Introductionmentioning

confidence: 99%

A neighbourhood level mortality classification of England and Wales, 2006–2009

2014

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The paper provides an overview of a neighbourhood level classification of mortality for England and Wales (2006-2009). Standardised mortality ratios for 63 causes of death were calculated for middle super output areas (weighted by prevalence). A k-means partitional method was used to classify the data. An eight cluster solution was found to best segment mortality patterns. Clusters mostly differentiated in terms of prevalence, however the importance of neurodegenerative diseases and causes related to unhealthy behaviours were important. The results describe a neighbourhood classification that can be an important tool to help inform policy development, resource allocation and targeting of services.

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Section: Introductionmentioning

confidence: 99%

A neighbourhood level mortality classification of England and Wales, 2006–2009

2014

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“…There have been many attempts to measure deprivation such as the Townsend Material Deprivation Index (Townsend et al 1988), the Carstairs Deprivation Index (Carstairs andMorris 1989, 1991) The WIMD gives an overall value for each of the 1896 lower super output areas, and comprises seven domains. Lower super output areas are the next level above Census enumeration districts (see Vickers and Rees (2007) for The domains that make up the WIMD are: access to services; education, skills and training; employment; health; housing; income, and physical environment. For this study, the WIMD was chosen because it is the index used by the Welsh Assembly Government, and it has a domain which focuses on health, which is most relevant here.…”

Section: Methodsmentioning

confidence: 99%

The geography of genetics: an analysis of referral patterns to a cancer genetics service

McDonald

Iredale

Higgs

2007

HUGO J

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This study uses a geographical information system (GIS) and statistical analysis to look for patterns in referrals to a British cancer genetics service. In this case, familial cancers are taken to be those that can develop when an individual inherits DNA mutations that cause an increased risk of cancer. Between 1998 and 2006 the Cancer Genetics Service for Wales received nearly 11,000 referrals for patients resident in Wales and it is the service database recording those referrals which is the subject of this secondary analysis. Using postcodes to match referred patients to areas, deprivation scores were assigned. Referral rates per 10,000 head of population across the 8-year study period by unitary authority are presented, as is information on referrals from primary and secondary care sources by year. Each patient referred has their family history of cancer recorded and is assigned to a risk category; high, medium or average. There are correlations between number of GPs (General Practitioners) in a practice, number of patients referred from a practice, and deprivation as measured by the overall Welsh Index of Multiple Deprivation 2005, such that the two former factors increase as deprivation decreases. Over time there were changes in referral sources, with referrals from primary care overtaking those from secondary care in percentage and absolute terms. There were also changes in the types of cancer referred, risk categories seen and to which centre referrals were made. Referral patterns reveal an inverse relationship between deprivation and health service availability and use.

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“…So returning to the original question, why are the general purpose local authority classifications such as those produced by ONS and Vickers et al (2007) not entirely suitable for internal migration analysis? As has already been noted, a number of pieces of work have made use of district level area classifications in their analysis.…”

Section: The Case For a New Classificationmentioning

confidence: 99%

A new area classification for understanding internal migration in Britain

Dennett

Stillwell

2011

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This article details the development of a new area classification for Britain based on internal migration variables taken from the 2001 Census. An explanation of why general-purpose area classifications already in existence are not ideal for internal migration analysis is provided, before an account of the construction of the new classification is given. The latter involves justification of the choice of variables, explanation of the methodology adopted and presentation of the final typology.

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Creating the UK National Statistics 2001 Output Area Classification

Cited by 169 publications

References 10 publications

A neighbourhood level mortality classification of England and Wales, 2006–2009

A neighbourhood level mortality classification of England and Wales, 2006–2009

The geography of genetics: an analysis of referral patterns to a cancer genetics service

A new area classification for understanding internal migration in Britain

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