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Predictive performance of comorbidity measures in administrative databases for diabetes cohorts

Overview of attention for article published in BMC Health Services Research, September 2013
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1 tweeter

Citations

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14 Dimensions

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46 Mendeley
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Title
Predictive performance of comorbidity measures in administrative databases for diabetes cohorts
Published in
BMC Health Services Research, September 2013
DOI 10.1186/1472-6963-13-340
Pubmed ID
Authors

Lisa M Lix, Jacqueline Quail, Opeyemi Fadahunsi, Gary F Teare

Abstract

The performance of comorbidity measures for predicting mortality in chronic disease populations and using ICD-9 diagnosis codes in administrative health data has been investigated in several studies, but less is known about predictive performance with ICD-10 data and for other health outcomes. This study investigated predictive performance of five comorbidity measures for population-based diabetes cohorts in administrative data. The objectives were to evaluate performance for: (a) disease-specific and general health outcomes, (b) data based on the ICD-9 and ICD-10 diagnoses, and (c) different age groups.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 4%
Australia 1 2%
Netherlands 1 2%
Spain 1 2%
Unknown 41 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 24%
Student > Bachelor 7 15%
Unspecified 4 9%
Professor > Associate Professor 4 9%
Student > Master 4 9%
Other 16 35%
Readers by discipline Count As %
Medicine and Dentistry 25 54%
Mathematics 7 15%
Unspecified 5 11%
Social Sciences 4 9%
Agricultural and Biological Sciences 2 4%
Other 3 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 September 2013.
All research outputs
#2,669,831
of 5,036,908 outputs
Outputs from BMC Health Services Research
#1,476
of 2,240 outputs
Outputs of similar age
#49,373
of 99,150 outputs
Outputs of similar age from BMC Health Services Research
#61
of 89 outputs
Altmetric has tracked 5,036,908 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,240 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 99,150 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.