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Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215, USA
Pfizer, Inc., New York, NY 10017, USA
Kaiser Permanente Georgia, Atlanta, GA 30305, USA
OptumInsight, Waltham, MA 02451, USA
Harvard School of Public Health, Boston, MA 02115, USA
Meyers Primary Care Institute, University of Massachusetts Medical School, the Meyers Primary Care Institute, Fallon Community Health Plan, Worcester, MA 01605, USA
Center for Health Studies, Group Health Cooperative, Seattle, WA 98101, USA
Lovelace Clinic Foundation, Albuquerque, NM 87106, USA
Kaiser Permanente Northern California, Oakland, CA 94611, USA
HealthPartners Research Foundation, Minneapolis, MN 55440, USA
Kaiser Permanente Colorado, Denver, CO 80237, USA
Kaiser Permanente Northwest, Portland OR 97227, USA
* Author to whom correspondence should be addressed.
Received: 6 November 2012; in revised form: 1 March 2013 / Accepted: 4 March 2013 / Published: 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
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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.
Brown JS, Petronis KR, Bate A, Zhang F, Dashevsky I, Kulldorff M, Avery TR, Davis RL, Chan KA, Andrade SE, Boudreau D, Gunter MJ, Herrinton L, Pawloski PA, Raebel MA, 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(1):179-200.
Brown, Jeffrey S.; Petronis, Kenneth R.; Bate, Andrew; Zhang, Fang; Dashevsky, Inna; Kulldorff, Martin; Avery, Taliser R.; Davis, Robert L.; Chan, K. A.; Andrade, Susan E.; Boudreau, Denise; Gunter, Margaret J.; Herrinton, Lisa; Pawloski, Pamala A.; Raebel, Marsha A.; Roblin, Douglas; Smith, David; Reynolds, Robert. 2013. "Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic." Pharmaceutics 5, no. 1: 179-200.