Person:

Kulldorff, Martin

Abstract Purpose To develop the infrastructure to conduct timely active surveillance for safety of influenza vaccines and other medical countermeasures in the Sentinel System (formerly the Mini‐Sentinel Pilot), a Food and Drug Administration‐sponsored national surveillance system that typically relies on data that are mature, settled, and updated quarterly. Methods: Three Data Partners provided their earliest available (“fresh”) cumulative claims data on influenza vaccination and health outcomes 3–4 times on a staggered basis during the 2013–2014 influenza season, collectively producing 10 data updates. We monitored anaphylaxis in the entire population using a cohort design and seizures in children ≤4 years of age using both a self‐controlled risk interval design (primary) and a cohort design (secondary). After each data update, we conducted sequential analysis for inactivated (IIV) and live (LAIV) influenza vaccines using the Maximized Sequential Probability Ratio Test, adjusting for data‐lag. Results: Most of the 10 sequential analyses were conducted within 6 weeks of the last care‐date in the cumulative dataset. A total of 6 682 336 doses of IIV and 782 125 doses of LAIV were captured. The primary analyses did not identify any statistical signals following IIV or LAIV. In secondary analysis, the risk of seizures was higher following concomitant IIV and PCV13 than historically after IIV in 6‐ to 23‐month‐olds (relative risk = 2.7), which requires further investigation. Conclusions: The Sentinel System can implement a sequential analysis system that uses fresh data for medical product safety surveillance. Active surveillance using sequential analysis of fresh data holds promise for detecting clinically significant health risks early. Limitations of employing fresh data for surveillance include cost and the need for careful scrutiny of signals. © 2015 The Authors. Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd.

Objective: To perform sample size calculations when using tree-based scan statistics in longitudinal observational databases. Methods: Tree-based scan statistics enable data mining on epidemiologic datasets where thousands of disease outcomes are organized into hierarchical tree structures with automatic adjustment for multiple testing. We show how to evaluate the statistical power of the unconditional and conditional Poisson versions. The null hypothesis is that there is no increase in the risk for any of the outcomes. The alternative is that one or more outcomes have an excess risk. We varied the excess risk, total sample size, frequency of the underlying event rate, and the level of across-the-board health care utilization. We also quantified the reduction in statistical power resulting from specifying a risk window that was too long or too short. Results: For 500,000 exposed people, we had at least 98 percent power to detect an excess risk of 1 event per 10,000 exposed for all outcomes. In the presence of potential temporal confounding due to across-the-board elevations of health care utilization in the risk window, the conditional tree-based scan statistic controlled type I error well, while the unconditional version did not. Discussion: Data mining analyses using tree-based scan statistics expand the pharmacovigilance toolbox, ensuring adequate monitoring of thousands of outcomes of interest while controlling for multiple hypothesis testing. These power evaluations enable investigators to design and optimize implementation of retrospective data mining analyses.

Abstract The self-controlled tree-temporal scan statistic—a new signal-detection method—can evaluate whether any of a wide variety of health outcomes are temporally associated with receipt of a specific vaccine, while adjusting for multiple testing. Neither health outcomes nor postvaccination potential periods of increased risk need be prespecified. Using US medical claims data in the Food and Drug Administration’s Sentinel system, we employed the method to evaluate adverse events occurring after receipt of quadrivalent human papillomavirus vaccine (4vHPV). Incident outcomes recorded in emergency department or inpatient settings within 56 days after first doses of 4vHPV received by 9- through 26.9-year-olds in 2006–2014 were identified using International Classification of Diseases, Ninth Revision, diagnosis codes and analyzed by pairing the new method with a standard hierarchical classification of diagnoses. On scanning diagnoses of 1.9 million 4vHPV recipients, 2 statistically significant categories of adverse events were found: cellulitis on days 2–3 after vaccination and “other complications of surgical and medical procedures” on days 1–3 after vaccination. Cellulitis is a known adverse event. Clinically informed investigation of electronic claims records of the patients with “other complications” did not suggest any previously unknown vaccine safety problem. Considering that thousands of potential short-term adverse events and hundreds of potential risk intervals were evaluated, these findings add significantly to the growing safety record of 4vHPV.