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. 2017 Mar;18(2):227-241.
doi: 10.1007/s10198-016-0769-2. Epub 2016 Mar 1.

Assessing asthma severity based on claims data: a systematic review

Christian Jacob  1 , Jennifer S Haas  2 , Benno Bechtel  3 , Peter Kardos  4 , Sebastian Braun  2
Affiliations

Assessing asthma severity based on claims data: a systematic review

Christian Jacob et al. Eur J Health Econ. 2017 Mar.

Abstract

Introduction: Asthma is one of the most common chronic diseases in Germany. Substantial economic evaluation of asthma cost requires knowledge of asthma severity, which is in general not part of claims data. Algorithms need to be defined to use this data source.

Aims and objectives: The aim of this study was to systematically review the international literature to identify algorithms for the stratification of asthma patients according to disease severity based on available information in claims data.

Methods: A systematic literature review was conducted in September 2015 using the DIMDI SmartSearch, a meta search engine including several databases with a national and international scope, e.g. BIOSIS, MEDLINE, and EMBASE. Claims data based studies that categorize asthma patients according to their disease severity were identified.

Results: The systematic research yielded 54 publications assessing asthma severity based on claims data. Thirty-nine studies used a standardized algorithm such as HEDIS, Leidy, the GINA based approach or CACQ. Sixteen publications applied a variety of different criteria for the severity categorisation such as asthma diagnoses, asthma-related drug prescriptions, emergency department visits, and hospitalisations.

Conclusion: There is no best practice method for the categorisation of asthma severity with claims data. Rather, a combination of algorithms seems to be a pragmatic approach. A transfer to the German context is not entirely possible without considering particular conditions associated with German claims data.

Keywords: Asthma; Claims data; Economic evaluation; Exacerbation; GINA; HEDIS; Intermittent; Leidy; Persistent; Systematic review.

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Conflict of interest statement

The study was funded by an unrestricted grant from GlaxoSmithKline. Benno Bechtel is an employee of GlaxoSmithKline. Jennifer Scarlet Haas, Christian Jacob and Sebastian Braun are employed by Xcenda GmbH, which received funding for the conduct of the study from GlaxoSmithKline. Peter Kardos received consulting fees for the conduct of the study from Xcenda GmbH.

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