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Logo of bmcmidmBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Medical Informatics and Decision Making
 
BMC Med Inform Decis Mak. 2012; 12: 3.
Published online Jan 23, 2012. doi:  10.1186/1472-6947-12-3
PMCID: PMC3353229
Use of name recognition software, census data and multiple imputation to predict missing data on ethnicity: application to cancer registry records
Ronan Ryan,corresponding author1 Sally Vernon,2 Gill Lawrence,3 and Sue Wilson4
1Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham B15 2TT UK
2Eastern Cancer Registration and Information Centre, Public Health Building, Unit C - Magog Court, Shelford Bottom, Hinton Way, Cambridge, CB22 3AD UK
3West Midlands Cancer Intelligence Unit, Public Health Building, University of Birmingham, Birmingham B15 2TT UK
4Primary Care Clinical Sciences, University of Birmingham, Birmingham B15 2TT UK
corresponding authorCorresponding author.
Ronan Ryan: r.p.ryan/at/bham.ac.uk; Sally Vernon: sally.vernon/at/ecric.nhs.uk; Gill Lawrence: gill.lawrence/at/wmciu.nhs.uk; Sue Wilson: s.wilson/at/bham.ac.uk
Received May 18, 2011; Accepted January 23, 2012.
Abstract
Background
Information on ethnicity is commonly used by health services and researchers to plan services, ensure equality of access, and for epidemiological studies. In common with other important demographic and clinical data it is often incompletely recorded. This paper presents a method for imputing missing data on the ethnicity of cancer patients, developed for a regional cancer registry in the UK.
Methods
Routine records from cancer screening services, name recognition software (Nam Pehchan and Onomap), 2001 national Census data, and multiple imputation were used to predict the ethnicity of the 23% of cases that were still missing following linkage with self-reported ethnicity from inpatient hospital records.
Results
The name recognition software were good predictors of ethnicity for South Asian cancer cases when compared with data on ethnicity derived from hospital inpatient records, especially when combined (sensitivity 90.5%; specificity 99.9%; PPV 93.3%). Onomap was a poor predictor of ethnicity for other minority ethnic groups (sensitivity 4.4% for Black cases and 0.0% for Chinese/Other ethnic groups). Area-based data derived from the national Census was also a poor predictor non-White ethnicity (sensitivity: South Asian 7.4%; Black 2.3%; Chinese/Other 0.0%; Mixed 0.0%).
Conclusions
Currently, neither method for assigning individuals to an ethnic group (name recognition and ethnic distribution of area of residence) performs well across all ethnic groups. We recommend further development of name recognition applications and the identification of additional methods for predicting ethnicity to improve their precision and accuracy for comparisons of health outcomes. However, real improvements can only come from better recording of ethnicity by health services.
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