Article
Detecting adverse drug reaction signals based on claims data – a review of the literature
Search Medline for
Authors
-
Jochen Walker
- Elsevier Health Analytics, Berlin, Germany
-
Josephine Jacob
- Elsevier Health Analytics, Berlin, Germany
-
Matthias Ganninger
- Elsevier Health Analytics, Berlin, Germany
| Published: | November 18, 2014 |
|---|
Outline
Text
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.
References
- 1.
- Avery TR, Kulldorff M, Vilk Y, Li L, Cheetham TC, Dublin S, Davis RL, Liu L, Herrinton L, Brown JS. Near real-time adverse drug reaction surveillance within population-based health networks: methodology considerations for data accrual. Pharmacoepidemiol Drug Saf. 2013 May;22(5):488-95. DOI: 10.1002/pds.3412
- 2.
- Brown JS, Kulldorff M, Chan KA, Davis RL, Graham D, Pettus PT, Andrade SE, Raebel MA, Herrinton L, Roblin D, Boudreau D, Smith D, Gurwitz JH, Gunter MJ, Platt R. Early detection of adverse drug events within population-based health networks: application of sequential testing methods. Pharmacoepidemiol Drug Saf. 2007 Dec;16(12):1275-84. DOI: 10.1002/pds.1509
- 3.
- Brown JS, Kulldorff M, Petronis KR, Reynolds R, Chan KA, Davis RL, Graham D, Andrade SE, Raebel MA, Herrinton L, Roblin D, Boudreau D, Smith D, Gurwitz JH, Gunter MJ, Platt R. Early adverse drug event signal detection within population-based health networks using sequential methods: key methodologic considerations. Pharmacoepidemiol Drug Saf. 2009 Mar;18(3):226-34. DOI: 10.1002/pds.1706
- 4.
- Choi NK, Chang Y, Choi YK, Hahn S, Park BJ. Signal detection of rosuvastatin compared to other statins: data-mining study using national health insurance claims database. Pharmacoepidemiol Drug Saf. 2010 Mar;19(3):238-46. DOI: 10.1002/pds.1902
- 5.
- Choi NK, Chang Y, Kim JY, Choi YK, Park BJ. Comparison and validation of data-mining indices for signal detection: using the Korean national health insurance claims database. Pharmacoepidemiol Drug Saf. 2011 Dec;20(12):1278-86. DOI: 10.1002/pds.2237
- 6.
- Coloma PM, Trifirò G, Schuemie MJ, Gini R, Herings R, Hippisley-Cox J, Mazzaglia G, Picelli G, Corrao G, Pedersen L, van der Lei J, Sturkenboom M; EU-ADR Consortium. Electronic healthcare databases for active drug safety surveillance: is there enough leverage? Pharmacoepidemiol Drug Saf. 2012 Jun;21(6):611-21. DOI: 10.1002/pds.3197
- 7.
- Curtis JR, Cheng H, Delzell E, Fram D, Kilgore M, Saag K, Yun H, Dumouchel W. Adaptation of Bayesian data mining algorithms to longitudinal claims data: coxib safety as an example. Med Care. 2008 Sep;46(9):969-75. DOI: 10.1097/MLR.0b013e318179253b
- 8.
- Kim J, Kim M, Ha JH, Jang J, Hwang M, Lee BK, Chung MW, Yoo TM, Kim MJ. Signal detection of methylphenidate by comparing a spontaneous reporting database with a claims database. Regul Toxicol Pharmacol. 2011 Nov;61(2):154-60. DOI: 10.1016/j.yrtph.2011.03.015
- 9.
- Li L. A conditional sequential sampling procedure for drug safety surveillance. Stat Med. 2009 Nov;28(25):3124-38. DOI: 10.1002/sim.3689
- 10.
- Nadkarni PM. Drug safety surveillance using de-identified EMR and claims data: issues and challenges. J Am Med Inform Assoc. 2010 Nov-Dec;17(6):671-4. DOI: 10.1136/jamia.2010.008607
- 11.
- Pratt NL, Ilomäki J, Raymond C, Roughead EE. The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study. BMC Med Res Methodol. 2014;14:66. DOI: 10.1186/1471-2288-14-66
- 12.
- Suling M, Pigeot I. Signal detection and monitoring based on longitudinal healthcare data. Pharmaceutics. 2012;4(4):607-40. DOI: 10.3390/pharmaceutics4040607
- 13.
- Wahab IA, Pratt NL, Wiese MD, Kalisch LM, Roughead EE. The validity of sequence symmetry analysis (SSA) for adverse drug reaction signal detection. Pharmacoepidemiol Drug Saf. 2013 May;22(5):496-502. DOI: 10.1002/pds.3417
- 14.
- Wahab IA, Pratt NL, Kalisch LM, Roughead EE. Comparing time to adverse drug reaction signals in a spontaneous reporting database and a claims database: a case study of rofecoxib-induced myocardial infarction and rosiglitazone-induced heart failure signals in Australia. Drug Saf. 2014 Jan;37(1):53-64. DOI: 10.1007/s40264-013-0124-9
