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Methods for identifying 30 chronic conditions: application to administrative data

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)

Mentioned by

twitter
11 tweeters

Citations

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100 Dimensions

Readers on

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120 Mendeley
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Title
Methods for identifying 30 chronic conditions: application to administrative data
Published in
BMC Medical Informatics and Decision Making, April 2015
DOI 10.1186/s12911-015-0155-5
Pubmed ID
Authors

Marcello Tonelli, Natasha Wiebe, Martin Fortin, Bruce Guthrie, Brenda R Hemmelgarn, Matthew T James, Scott W Klarenbach, Richard Lewanczuk, Braden J Manns, Paul Ronksley, Peter Sargious, Sharon Straus, Hude Quan

Abstract

Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity. We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as "high validity"; those with positive predictive value ≥70% and sensitivity <70% were graded as "moderate validity". To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year. Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities. We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Portugal 1 <1%
France 1 <1%
Unknown 117 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 21%
Student > Ph. D. Student 22 18%
Student > Master 17 14%
Student > Bachelor 11 9%
Student > Doctoral Student 8 7%
Other 22 18%
Unknown 15 13%
Readers by discipline Count As %
Medicine and Dentistry 46 38%
Social Sciences 10 8%
Agricultural and Biological Sciences 8 7%
Psychology 5 4%
Economics, Econometrics and Finance 4 3%
Other 24 20%
Unknown 23 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 15 August 2019.
All research outputs
#2,356,993
of 14,280,939 outputs
Outputs from BMC Medical Informatics and Decision Making
#242
of 1,320 outputs
Outputs of similar age
#43,096
of 228,260 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 14,280,939 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,320 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 81% 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 228,260 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% 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