The validity of diagnostic algorithms to identify asthma patients in healthcare administrative databases: a systematic literature review

Abstract

Objectives

To review the available evidence supporting the validity of algorithms to identify asthma patients in healthcare administrative databases.

Methods

A systematic literature search was conducted on multiple databases from inception to March 2020 to identify studies that reported the validity of case-finding asthma algorithms applied to healthcare administrative data. Following an initial screening of abstracts, two investigators independently assessed the full text of studies which met the pre-determined eligibility criteria. Data on study population and algorithm characteristics were extracted. A revised version of the Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the risk of bias and generalizability of studies.

Results: A total of 20 studies met the eligibility criteria. Algorithms which incorporated ≥1 diagnostic code for asthma over a 1-year period appeared to be valid in both adult and pediatric populations (sensitivity ≥ 85%; specificity ≥ 89%; PPV ≥ 70%). The validity was enhanced when: (1) the time frame to capture asthma cases was increased to two years; (2) ≥2 asthma diagnostic codes were considered; and (3) when diagnoses were recorded by a pulmonologist. Algorithms which integrated pharmacy claims data appeared to correctly identify asthma patients; however, the extent to which asthma medications can improve the validity remains unclear. The quality of several studies was high, although disease progression bias and biases related to self-reported data was observed in some studies.

Conclusions

Healthcare administrative databases are adequate sources to identify asthma patients. More restrictive definitions based on both asthma diagnoses and asthma medications may enhance validity, although further research is required to confirm this hypothesis.

KEYWORDS:

• Asthma
• validation
• diagnostic algorithms
• administrative databases

Acknowledgements

We would like to thank library information specialists Ms. Kathy Rose and Ms. Nathalie Rheault who helped with the development of the electronic search strategies. Special thanks to Ms. Andrée-Anne Beaudoin, and M. Patrick Castonguay, who contributed to the development and validation of the search strategies, the study selection, and the data extraction.

Declaration of interest

AV reports grants from AstraZeneca Canada Inc. and personal fees from Janssen for studies unrelated to this work. AY received a doctoral training award from the Fonds de Recherche du Québec–Santé. LB reports research grants and professional fees from AstraZeneca, GlaxoSmithKline, and Genentech for studies unrelated to this work. AL reports a catalyst grant from the CIHR for research unrelated to this work. All other named authors (RD, MC, and AMC) have no conflict of interest, financial or otherwise.

Funding

This study was supported by the Quebec SPOR support Unit, an initiative funded by the Canadian Institutes of Health Research (CIHR), the Ministère de la santé et des services sociaux du Québec, and the Fonds de recherche du Québec-Santé. These three funders were not involved in the design, the data collection, the interpretation or the publication of the review.