Comparison of ethnic group classification using naming analysis and routinely collected data: application to cancer incidence trends in children and young people

Objective: Inpatient Hospital Episode Statistics (HES) ethnicity data are available but not always collected and data quality can be unreliable. This may have implications when assessing outcomes by ethnicity. An alternative method for assigning ethnicity is using naming algorithms. We investigate if the association between ethnicity and cancer incidence varied dependent upon how ethnic group was assigned. Design: Population-based cancer registry cohort study Setting: Yorkshire, UK Participants: Cancer registrations from 1998-2009 in children and young people (0-29 years) from a specialist cancer register in Yorkshire, UK (N=3998) were linked to inpatient HES data to obtain recorded ethnicity. Patient?s names, recorded in the cancer register, were matched to an ethnic group using the naming algorithm software Onomap. Each source of ethnicity was categorised as White, South Asian (SA) or Other and a further two indicators were defined based on the combined ethnicities of HES and Onomap, one prioritising HES results, the other prioritising Onomap. Outcomes: Incidence rate ratios (IRR) between ethnic groups were compared using Poisson regression for all cancers combined, leukaemia, lymphoma and central nervous system (CNS) tumours. Results: Depending on the indicator used, 7.1% to 8.6% of the study population were classified as SA. For all cancers there were no statistically significant differences between White and SA groups using any indicator, however for lymphomas significant differences were only evident using one of the ?Combined? indicators (IRR=1.36 (95%CI 1.08, 1.71)) and for CNS tumours incidence was lower using three of the four indicators. For the other ethnic group the IRR for all cancers ranged from 0.78 (0.65, 0.94) to 1.41 (1.23, 1.62). Conclusions: Using different methods of assigning ethnicity can result in different estimates of ethnic variation in cancer incidence. Combining ethnicity from multiple sources results in a more complete estimate of ethnicity than the use of one single source.