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Identifying incident colorectal and lung cancer cases in health service utilisation databases in Australia: a validation study

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2017
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  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

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6 tweeters

Citations

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26 Dimensions

Readers on

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37 Mendeley
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Title
Identifying incident colorectal and lung cancer cases in health service utilisation databases in Australia: a validation study
Published in
BMC Medical Informatics and Decision Making, February 2017
DOI 10.1186/s12911-017-0417-5
Pubmed ID
Authors

David Goldsbury, Marianne Weber, Sarsha Yap, Emily Banks, Dianne L. O’Connell, Karen Canfell

Abstract

Data from centralised, population-based statutory cancer registries are generally considered the 'gold standard' for confirming incident cases of cancer. When these are not available, or more current information is needed, hospital or other routinely collected population-level data may be feasible alternative sources. We aimed to determine the validity of various methods using routinely collected administrative health data for ascertaining incident cases of colorectal or lung cancer in participants from the 45 and Up Study in New South Wales (NSW), Australia. For 266,844 participants in the 45 and Up Study (recruited 2006-2009) ascertainment of incident colorectal or lung cancers was assessed using diagnosis and treatment records in linked administrative health datasets (hospital, emergency department, Medicare and pharmaceutical claims, death records). This was compared with ascertainment via the NSW Cancer Registry (NSWCR, the 'gold standard') for a period for which both data sources were available for participants. A total of 2253 colorectal and 1019 lung cancers were recorded for study participants in the NSWCR over the period 2006-2010. A diagnosis of primary cancer recorded in the statewide Admitted Patient Data Collection identified the majority of NSWCR colorectal and lung cancers, with sensitivities and positive predictive values (PPV) of 95% and 91% for colorectal cancer and 81% and 85% for lung cancer, respectively. Using additional information on lung cancer deaths from death records increased sensitivity to 84% (PPV 83%) for lung cancer, but did not improve ascertainment of colorectal cancers. Hospital procedure codes for colorectal cancer surgery identified cases with sensitivity 81% and PPV 54%. No other individual indicator had sensitivity >50% or PPV >65% for either cancer type and no combination of indicators increased both the sensitivity and PPV above that achieved using the hospital cancer diagnosis data. All specificities were close to 100%; 95% confidence intervals for sensitivity and PPV were generally +/-2%. In NSW, identifying new cases of colorectal and lung cancer from administrative health datasets, such as hospital records, is a feasible alternative when cancer registry data are not available. However, the strengths and limitations of the different data sources should be borne in mind.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 24%
Student > Master 4 11%
Student > Bachelor 4 11%
Student > Doctoral Student 3 8%
Librarian 3 8%
Other 9 24%
Unknown 5 14%
Readers by discipline Count As %
Medicine and Dentistry 16 43%
Nursing and Health Professions 6 16%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Psychology 2 5%
Computer Science 1 3%
Other 3 8%
Unknown 7 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 March 2017.
All research outputs
#4,620,615
of 15,920,152 outputs
Outputs from BMC Medical Informatics and Decision Making
#505
of 1,450 outputs
Outputs of similar age
#90,806
of 262,520 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 15,920,152 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,450 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,520 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them