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Purpose: Previous studies have suggested a link between glucagon-like peptide 1 (GLP-1)-based therapies and acute pancreatitis, while other studies have found no association. Because differences in diabetes severity may confound this relationship, a self-controlled case series (SCCS) analysis has been suggested as a means to control for individual-level confounding., Methods: We evaluated the relationship between GLP-1-based therapies and pancreatitis by SCCS method using a large observational database. We calculated the incidence density ratio of pancreatitis for exposure versus non-exposure to each drug. To examine the robustness of our findings, we performed sensitivity analyses by varying risk windows, using two pancreatitis definitions and including incident pancreatitis or all occurrences., Results: From dispensing data on 1.2 million patients, we found 7992 sitagliptin-exposed patients and 3552 exenatide-exposed patients between 2004 and 2009. Using an ICD9/CPT-based case definition of pancreatitis, we identified 207 sitagliptin and 82 exenatide cases. Augmenting this definition with laboratory criteria increased our cohort to 245 sitagliptin and 96 exenatide cases. For sitagliptin and exenatide cases, respectively, the mean duration of observation was 5.2 and 5.5 years, and the mean duration of drug exposure was 0.7 and 0.5 years. For all analyses (including different pancreatitis definitions, risk periods, and incident or recurrent events), the incidence density ratios for development of pancreatitis during exposure versus non-exposure ranged from 0.68 to 1.46, with all having 95% confidence intervals containing 1., Conclusions: We found no association between the use of GLP-1-based therapies and pancreatitis using SCCS analysis in a large observational database.

The spatial scan statistic has been widely used in spatial disease surveillance and spatial cluster detection for more than a decade. However, overdispersion often presents in real-world data, causing not only violation of the Poisson assumption but also excessive type I errors or false alarms. In order to account for overdispersion, we extend the Poisson-based spatial scan test to a quasi-Poisson-based test. The simulation shows that the proposed method can substantially reduce type I error probabilities in the presence of overdispersion. In a case study of infant mortality in Jiangxi, China, both tests detect a cluster; however, a secondary cluster is identified by only the Poisson-based test. It is recommended that a cluster detected by the Poisson-based scan test should be interpreted with caution when it is not confirmed by the quasi-Poisson-based test., (Copyright © 2011 John Wiley & Sons, Ltd.)