Artikel
Detecting adverse drug reaction signals based on claims data – a review of the literature
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Autoren
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Jochen Walker
- Elsevier Health Analytics, Berlin, Germany
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Josephine Jacob
- Elsevier Health Analytics, Berlin, Germany
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Matthias Ganninger
- Elsevier Health Analytics, Berlin, Germany
| Veröffentlicht: | 18. November 2014 |
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Gliederung
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Background: Post-marketing detection of potential adverse drug reactions (ADR) is a crucial task in pharmacovigilance. Spontaneous reports of patients and physicians constitute important sources to detect ADR signals early on. In the last years, large projects (e.g. EU-ADR or Mini-Sentinel) have investigated the usefulness of electronic health records for signal generation. In Germany, where electronic health record databases are not as widely available as in other countries, claims data could potentially supplement spontaneous reports to detect ADR signals.
A literature review was performed to obtain an overview of the existing evidence on this topic. This review constitutes the initial step of a planned study about the feasibility of using anonymized claims data from German sickness funds for signal detection of ADR.
Materials and Methods: In August 2014 a MEDLINE search was undertaken (via PUBMED). Three concepts (adverse drug reaction, signal detection and database) were selected to build the search string. Each concept was expanded by several synonyms and spelling variants (e.g. adverse reaction, adverse event, side-effect) and MeSH Terms were also included. The three concepts were connected by the „AND“-operator to explore the MEDLINE database without time restriction. Two reviewers reviewed the retrieved abstracts independently. Articles were included if they were in published in English or German and explored the use of claims data for detecting adverse drug reactions. A consensual approach was applied in order to identify articles for inclusion in this review. Articles listed as references in the selected papers were included for possible selection.
Results: In total, 320 citations, 303 abstracts, and 11 papers were reviewed. Three additional papers were identified by screening the references of the selected papers. Nine different methods were applied to generate signals based on claims data: (1) Bayes Multi-Item Gamma Poisson Shrinker (MGPS), (2) Bayesian Confidence Propagation Neural Network (BCPNN), (3) sequence symmetry analysis (SSA), (4) proportional claims ratio (PCR), (5) claims odds ratio (COR), (6) information component (IC), (7) relative risk (RR), (8) Chi-squared test, (9) maximized sequential probability ratio test (maxSPRT). Most oft the methods were adopted from data mining techniques also used for signal detection in spontaneous reporting databases. However, especially in the more recent articles, methods like SSA for longitudinal data were used. The validity of the different methods varied depending on the methods and the gold standard used for testing signal detection (Sensitivity: 0–61%, PPV: 0–77%).
Conclusion: This existing body of evidence on ADR signal detection in claims data offers only limited information. Further research in that area could aid the use of claims data as an additional source for drug safety surveillance in the future.
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