Evidence map›Paper›PMID 22074413›Full record

ArticleBMC public health2011

The distribution of lung cancer across sectors of society in the United Kingdom: a study using national primary care data.

Barbara Iyen-Omofoman, Richard B Hubbard, Chris J P Smith, Emily Sparks, Emma Bradley, Alison Bourke, Laila J Tata

Abstract read
In one paragraph

Article in BMC public health, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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  15. Validation of a coding algorithm to identify bladder cancer and distinguish stage in an electronic medical records database.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2015
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Barbara Iyen-OmofomanDivision of Epidemiology and Public health, University of Nottingham, Clinical Sciences Building, City Hospital, Nottingham, NG5 1PB, UK. mcxboi1@nottingham.ac.uk
Richard B Hubbard
Chris J P Smith
Emily Sparks
Emma Bradley
Alison Bourke
Laila J Tata

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleHumansIncidenceInfantLung NeoplasmsMaleMiddle AgedPopulation SurveillancePrimary Health CareUnited Kingdom

Identifiers

PMID22074413
PMCPMC3282683

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.