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Improving the ethnic classification of patient registers

Objectives of seminar. Promote awareness of existing tools for targetting health communications at ethnic' groupsIndividual patients on registersSurgeries and other contact pointsLocal promotional activities2. Discussion of ideas for collaborative use of individual patient registers to improv

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Improving the ethnic classification of patient registers

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    1. ‘Improving the ethnic classification of patient registers’ Centre for Advanced Spatial Analysis, UCL 25th May, 2005

    2. Objectives of seminar Promote awareness of existing tools for targetting health communications at ‘ethnic’ groups Individual patients on registers Surgeries and other contact points Local promotional activities 2. Discussion of ideas for collaborative use of individual patient registers to improve the quality of ‘ethnic’ coding 3. Forum for exchange of information Methods Ethics and other data protection issues

    3. Our conception of ‘Ethnicity’ Multi-variate classification based on a combination of : Cultural Origin – eg religion, beliefs Ethnicity – eg country of origin, diet Language

    4. Background to this seminar

    5. Context : CASA (1) 1970s - Neighbourhood classifications used for prioritising public sector initiatives 1990s - Application of postcode classifications adopted by commercial companies 2002 – CASA becomes involved in the application of Mosaic in health, policing and education 2003 CASA work with Dr Foster on Slough Diabetes project

    6. Context : CASA (2) 2004 – CASA sets up Knowledge Transfer Partnership (KTP) with Camden PCT to develop health applications of geodemographics 2004 – CASA wins ESRC grant for ‘quantitative analysis of names’ 2005 – Camden PCT develops capability in the application of ‘names’ as well as Mosaic to targetting of public health campaigns

    7. Contact details CASA website E/mail addresses Pablo Mateos – p.mateos@ucl.ac.uk Richard Webber – richardwebber@blueyonder.co.uk Paul Longley –p.longley@geog.ucl.ac.uk

    8. ‘Quantitative Analysis of Names’ ESRC funded project Use of surname as an identifier of cultural origin Regional origins of English names Regional distribution of Celtic names Current locations of names ‘imported from abroad’ Jewish Continental European and Hispanic Asian African Middle Eastern

    9. Identification of potential applications Academic / Social Scientific Study of meaning of names Studies of historic migration patterns Social mobility of Celtic migrants to England Policy applications Measurement of ‘social capital’ Differentiation of crude ‘South Asian’ definition Targetting of public sector communications programmes Auditing of equal opportunities in employment

    10. Key data files 40 million records Experian 1996 GB electoral roll First name Surname Postal area code Mosaic code 26 million records 1881 census Summary statistics on name frequencies by region from Anglophone diaspora US, Canada, Australia, New Zealand, North and Southern Ireland

    11. Geography of the name ‘Webber’

    12. % electors with occupational names

    13. % electors of Welsh surnames

    14. CEL assignment : Phase one Identify 25,000 surnames with > 100 occurrences in 1996 Assign to hierarchy English; general name type; detailed name type Celtic; country of origin; general type Imported from abroad; region of origin; country of origin

    15. Webber Level one : English ‘metonym’ Level two : Metonym ending in ‘-er’ Level three : Manufacturing occupation

    16. Zhang Level one : Imported from abroad Level two : East Asian Level three : Chinese

    18. Muslim and South Asian names (1462)

    19. Phase one assignment method (25,000 names with > 100 occurrences) General knowledge Identification of top postal area and level of concentration in it Identification of top Mosaic type and level of concentration in it Identification of concentration in 1881 Frequencies in other Anglophone countries

    20.

    23. Status and Asian names

    26. Output Directory assigning a Cultural / Language / Ethnicity code to each name with more than 100 occurrences on the GB electoral roll

    27. Phase two assignment (all surnames > 5 occurrences) Rank first names by frequency Allocate names to CEL categories where possible Identify for each surname the proportion of associated first names in known CEL categories

    28.

    29. Selected first names

    30. Output Database giving for 60,000 surnames ‘imported from abroad’ % electors by CEL of first name Most common cell (three level hierarchy) Database giving for 60,000 first names ‘imported from abroad’ (3.2m occurrences) % electors by CEL of surname Most common cell (three level hierarchy)

    31. Evaluation of solution Seems to work well for all ethnic groups other than Caribbeans CEL overlaps between surname and first name South Asians and Muslims – 80% Africans, Turks, Cypriots, Chinese – 50% Hispanics – 20% Other Europeans – 8 – 15% Jew – 4% Irish, Scots, Welsh – 3% High overlap between certain CELs within Muslim group Spain, Portugal, Italy Netherlands, Germany and Czech Republic Confusion among serial migrant groups Hispanic migrants to India Chinese migrants to West Indies

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