Evidence map›Paper›PMID 29262809›Full record

ArticleBMC medical informatics and decision making2017

Using medication utilization information to develop an asthma severity classification model.

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

Abstract read
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Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Tsung-Hsien YuDepartment of Health Care Management, National Taipei University of Nursing and Science, No.89, Nei-Chiang St, Taipei, Taiwan.
Pin-Kuei FuInstitute of Health Policy and Management, College of Public Health, National Taiwan University, No. 17, Xu-Zhou Rd, Taipei, Taiwan.
Yu-Chi TungInstitute of Health Policy and Management, College of Public Health, National Taiwan University, No. 17, Xu-Zhou Rd, Taipei, Taiwan. yuchitung@ntu.edu.tw.ORCID http://orcid.org/0000-0001-9031-8749

Funding

Ministry of Science and Technology, Taiwan MOST- 106-2628-H-227 -001
6 · The paper itself

Abstract

backgroundClaims 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.

methodsThe 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.

resultsIn 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.

conclusionsUnderstanding 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.

Indexed as

Models, TheoreticalSeverity of Illness IndexAdolescentAdultAgedAsthmaComorbidityDrug PrescriptionsFemaleHumansMaleMiddle AgedNational Health ProgramsTaiwanYoung AdultAsthmaClaims dataMedication utilizationSeverity classification model development

Identifiers

PMID29262809
PMCPMC5738714

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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.