Abstract
BackgroundThere is pressing need to diagnose lung cancer earlier in the United Kingdom (UK) and it is likely that research using computerised general practice records will help this process. Linkage of these records to area-level geo-demographic classifications may also facilitate case ascertainment for public health programmes, however, there have as yet been no extensive studies of data validity for such purposes.MethodsTo first address the need for validation, we assessed the completeness and representativeness of lung cancer data from The Health Improvement Network (THIN) national primary care database by comparing incidence and survival between 2000 and 2009 with the UK National Cancer Registry and the National Lung Cancer Audit Database. Secondly, we explored the potential of a geo-demographic social marketing tool to facilitate disease ascertainment by using Experian's Mosaic Public Sector ™ classification, to identify detailed profiles of the sectors of society where lung cancer incidence was highest.ResultsOverall incidence of lung cancer (41.4/100, 000 person-years, 95% confidence interval 40.6-42.1) and median survival (232 days) were similar to other national data; The incidence rate in THIN from 2003-2006 was found to be just over 93% of the national cancer registry rate. Incidence increased considerably with area-level deprivation measured by the Townsend Index and was highest in the North-West of England (65.1/100, 000 person-years). Wider variations in incidence were however identified using Mosaic classifications with the highest incidence in Mosaic Public Sector ™types 'Cared-for pensioners, ' 'Old people in flats' and 'Dignified dependency' (191.7, 174.2 and 117.1 per 100, 000 person-years respectively).ConclusionsRoutine electronic data in THIN are a valid source of lung cancer information. Mosaic ™ identified greater incidence differentials than standard area-level measures and as such could be used as a tool for public health programmes to ascertain future cases more effectively.
Highlights
There is pressing need to diagnose lung cancer earlier in the United Kingdom (UK) and it is likely that research using computerised general practice records will help this process
Compared with the wellknown and commonly used Townsend Index [14] which measures the area-based level of material deprivation using four indicators: unemployment, car ownership, house ownership and overcrowding, Mosaic Public Sector TM classifications take account of more granular characteristics of the population living at different UK postcodes and allows a clearer identification of the characteristics and differing needs of people [15]
To assess the completeness of lung cancer ascertainment in The Health Improvement Network (THIN) general practices and whether this varied by different UK Strategic Health Authority (SHA) regions, we calculated the THIN lung cancer incidence rates from 2006-2008 for each SHA and compared these with the 2003-2007 lung cancer rates recorded by the National Cancer Registry [20]
Summary
There is pressing need to diagnose lung cancer earlier in the United Kingdom (UK) and it is likely that research using computerised general practice records will help this process. Linkage of these records to arealevel geo-demographic classifications may facilitate case ascertainment for public health programmes, there have as yet been no extensive studies of data validity for such purposes. Compared with the wellknown and commonly used Townsend Index [14] which measures the area-based level of material deprivation using four indicators: unemployment, car ownership, house ownership and overcrowding, Mosaic Public Sector TM classifications take account of more granular characteristics of the population living at different UK postcodes and allows a clearer identification of the characteristics and differing needs of people [15]. Mosaic classification has been used to a limited extent for the targeting of population public health services to those most in need [16] and studies have usefully applied it to demonstrate social disparities in health-related behaviours such as heavy episodic drinking [17] and smoking prevalence [18]
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