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Using medication utilization information to develop an asthma severity classification model

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2017
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Title
Using medication utilization information to develop an asthma severity classification model
Published in
BMC Medical Informatics and Decision Making, December 2017
DOI 10.1186/s12911-017-0571-9
Pubmed ID
Authors

Tsung-Hsien Yu, Pin-Kuei Fu, Yu-Chi Tung

Abstract

Claims data are currently widely used as source data in asthma studies. However, the insufficient information in claims data related to level of asthma severity may negatively impact study findings. The present study develops and validates an asthma severity classification model that uses medication utilization in Taiwan National Health Insurance claims data. The National Health Insurance Research Database was used for the years 2006-2012 and included a total of 7221 patients newly diagnosed with asthma in 2007 for model development and in 2008 for model validation. The medication utilization of patients during the first year after the index date was used to classify level of severity, and the acute exacerbation of asthma during the second through fourth years after the index date was used as the outcome variable. Three models were developed, with subjects classified into four, three, and two groups, respectively. The area under the receiver operating characteristic curve (AUC) and the Kaplan-Meier survival curve were used to compare the performances of the classification models. In development data, the distribution of subjects and acute exacerbation rate among the stage 1 to stage 4 were: 62.71%, 5.54%, 22.79%, and 8.96%, and 8.17%, 9.55%, 11.97%, and 14.91%, respectively. The results also showed the higher severity groups to be more prone to being prescribed oral corticosteroids for asthma control, while lower severity groups were more likely to be prescribed short-acting medication and inhaled corticosteroid treatment. Furthermore, the results of survival analysis showed two-group classification was recommended and yield moderate performance (AUC = 0.671). In validation data, the distribution of subjects, acute exacerbation rates, and medication uses among stages were similar to those in development data, and the results of survival analysis were also the same. Understanding asthma severity is critical to conducting effective, scholarly research on asthma, which currently uses claims data as a primary data source. The model developed in the present study not only overcomes a gap in the current literature but also provides an opportunity to improve the validity and quality of claims-data-based asthma studies.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 17%
Researcher 4 17%
Student > Ph. D. Student 3 13%
Lecturer > Senior Lecturer 1 4%
Student > Doctoral Student 1 4%
Other 4 17%
Unknown 6 26%
Readers by discipline Count As %
Medicine and Dentistry 9 39%
Business, Management and Accounting 2 9%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Nursing and Health Professions 1 4%
Economics, Econometrics and Finance 1 4%
Other 1 4%
Unknown 8 35%

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 01 September 2018.
All research outputs
#10,263,769
of 13,454,271 outputs
Outputs from BMC Medical Informatics and Decision Making
#950
of 1,216 outputs
Outputs of similar age
#255,788
of 386,397 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#87
of 112 outputs
Altmetric has tracked 13,454,271 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 112 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.