Evidence map›Paper›PMID 32819994›Full record

ArticleBMJ open2020

What are the implications of using individual and combined sources of routinely collected data to identify and characterise incident site-specific cancers? a concordance and validation study using linked English electronic health records data.

Helen Strongman, Rachael Williams, Krishnan Bhaskaran

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
1.2field-weighted citation impact, top 19% of its field
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

28 citing papers in PubMed, 43 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Helen StrongmanDepartment of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK helen.strongman@lshtm.ac.uk.ORCID 0000-0001-8739-4601
Rachael WilliamsClinical Practice Research Datalink (CPRD), Medicines and Healthcare Products Regulatory Agency, London, UK.
Krishnan BhaskaranDepartment of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
London School of Hygiene & Tropical Medicine · GBClinical Practice Research Datalink · GB

Funding

Wellcome TrustWellcome Trust 107731/Z/15/Z
6 · The paper itself

Abstract

objectivesTo describe the benefits and limitations of using individual and combinations of linked English electronic health data to identify incident cancers. DESIGN AND

settingOur descriptive study uses linked English Clinical Practice Research Datalink primary care; cancer registration; hospitalisation and death registration data. PARTICIPANTS AND MEASURES: We implemented case definitions to identify first site-specific cancers at the 20 most common sites, based on the first ever cancer diagnosis recorded in each individual or commonly used combination of data sources between 2000 and 2014. We calculated positive predictive values and sensitivities of each definition, compared with a gold standard algorithm that used information from all linked data sets to identify first cancers. We described completeness of grade and stage information in the cancer registration data set.

results165 953 gold standard cancers were identified. Positive predictive values of all case definitions were ≥80% and ≥94% for the four most common cancers (breast, lung, colorectal and prostate). Sensitivity for case definitions that used cancer registration alone or in combination was ≥92% for the four most common cancers and ≥80% across all cancer sites except bladder cancer (65% using cancer registration alone). For case definitions using linked primary care, hospitalisation and death registration data, sensitivity was ≥89% for the four most common cancers, and ≥80% for all cancer sites except kidney (69%), oral cavity (76%) and ovarian cancer (78%). When primary care or hospitalisation data were used alone, sensitivities were generally lower and diagnosis dates were delayed. Completeness of staging data in cancer registration data was high from 2012 (minimum 76.0% in 2012 and 86.4% in 2014 for the four most common cancers).

conclusionsAscertainment of incident cancers was good when using cancer registration data alone or in combination with other data sets, and for the majority of cancers when using a combination of primary care, hospitalisation and death registration data.

Indexed as

Electronic Health RecordsNeoplasmsHumansMaleRegistriesRoutinely Collected Health DataSemantic Webepidemiologyhealth informaticsoncologystatistics & research methods

Identifiers

PMID32819994
PMCPMC7443310
OpenAlexW3080100206

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.