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Pharmaceutics 2013, 5(1), 179-200; doi:10.3390/pharmaceutics5010179

Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

1
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA
3
Pfizer, Inc., New York, NY 10017, USA
4
Kaiser Permanente Georgia, Atlanta, GA 30305, USA
5
OptumInsight, Waltham, MA 02451, USA
6
Harvard School of Public Health, Boston, MA 02115, USA
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Meyers Primary Care Institute, University of Massachusetts Medical School, the Meyers Primary Care Institute, Fallon Community Health Plan, Worcester, MA 01605, USA
8
Center for Health Studies, Group Health Cooperative, Seattle, WA 98101, USA
9
Lovelace Clinic Foundation, Albuquerque, NM 87106, USA
10
Kaiser Permanente Northern California, Oakland, CA 94611, USA
11
HealthPartners Research Foundation, Minneapolis, MN 55440, USA
12
Kaiser Permanente Colorado, Denver, CO 80237, USA
13
Kaiser Permanente Northwest, Portland OR 97227, USA
*
Author to whom correspondence should be addressed.
Received: 6 November 2012 / Revised: 1 March 2013 / Accepted: 4 March 2013 / Published: 14 March 2013
(This article belongs to the Special Issue Drug Safety and Pharmacovigilance)
View Full-Text   |   Download PDF [262 KB, 15 March 2013; original version 14 March 2013]   |  

Abstract

Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.
Keywords: pharmacovigilance; drug safety surveillance; adverse events data mining; gamma Poisson shrinkage; tree-based scan statistic pharmacovigilance; drug safety surveillance; adverse events data mining; gamma Poisson shrinkage; tree-based scan statistic
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Brown, J.S.; Petronis, K.R.; Bate, A.; Zhang, F.; Dashevsky, I.; Kulldorff, M.; Avery, T.R.; Davis, R.L.; Chan, K.A.; Andrade, S.E.; Boudreau, D.; Gunter, M.J.; Herrinton, L.; Pawloski, P.A.; Raebel, M.A.; Roblin, D.; Smith, D.; Reynolds, R. Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic. Pharmaceutics 2013, 5, 179-200.

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