Safety signal identification for COVID-19 bivalent booster vaccination using tree-based scan statistics in the Vaccine Safety Datalink |
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| Affiliation: | 1. Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States;2. Kaiser Permanente Colorado, Aurora, CO, United States;3. Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States;4. Kaiser Permanente Northern California, Oakland, CA, United States;5. Marshfield Clinic Research Institute, Marshfield, WI, United States;6. Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States;7. Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA, United States;8. Kaiser Permanente Northwest, Portland, OR, United States;9. HealthPartners Institute, Bloomington, MN, United States;10. Denver Health, Denver, CO, United States;1. Departmentof Health Sciences, University of Genoa, Genoa, Italy;2. ScientificAdvisor of UNESCO CHAIR “Anthropology of Health – Biosphere and Healing System”, Italy;3. University Museum System of Siena (Simus), History of Medicine, University of Siena, Siena, Italy;1. Department of Psychiatry, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czech Republic;1. Institute of Public Health, ZHAW Zurich University of Applied Sciences, Katharina-Sulzer-Platz 9, 8401 Winterthur, Switzerland;2. Swiss Paraplegic Research, Guido A. Zäch Strasse 4, 6207 Nottwil, Switzerland;3. BeChange Research Group, Institute of Communication and Public Policy and Institue of Public Health, Università della Svizzera italiana, Via Guiseppe Buffi 13, 6900 Lugano, Switzerland;4. Medical Faculty, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland;1. Department of Medicine, South Georgia Medical Center, 2501 N Patterson St., Valdosta, GA, United States;2. Pulmonary Critical Care, Albert Einstein College of Medicine, Bronx, NY, United States;3. Economics, Valdosta State University, Valdosta, GA, United States;4. Edward Via College of Osteopathic Medicine, Auburn, AL, United States;5. Philadelphia College of Osteopathic Medicine- Moultrie, GA, United States;1. Professor & Chief, Division of Occupational Medicine, Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, United States;2. Leonard Davis Institute, University of Pennsylvania Perelman School of Medicine, United States;3. Director & Adjunct Professor of Epidemiology & Biostatistics, Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, United States;4. Associate Vice President of Strategic Operations, University of Pennsylvania Perelman School of Medicine, United States;5. Assistant Professor, University of Pennsylvania, Perelman School of Medicine, Department of Medicine, Division of Infectious Diseases, United States;6. Chief Medical Officer and Senior Vice President, University of Pennsylvania Health System, United States;7. Visiting Research Scientist, Vaccine Education Center, Children’s Hospital of Philadelphia, United States;1. Tropical Health Department, High Institute of Public Health, Alexandria University, Egypt;2. Biostatistics and Demography Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt;3. Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Cairo University, Egypt;4. Epidemiology and Preventive Medicine Department, National Liver Institute, Menoufia University, Egypt;5. Institute of Graduate Studies & Research, Alexandria University, Egypt;6. Family Health Department, High Institute of Public Health, Alexandria University, Egypt |
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| Abstract: | BackgroundTraditional active vaccine safety monitoring involves pre-specifying health outcomes and biologically plausible outcome-specific time windows of concern, limiting the adverse events that can be evaluated. In this study, we used tree-based scan statistics to look broadly for >60,000 possible adverse events after bivalent COVID-19 vaccination.MethodsVaccine Safety Datalink enrollees aged ≥5 years receiving Moderna or Pfizer-BioNTech bivalent COVID-19 vaccine through November 2022 were followed for 56 days post-vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within the hierarchical ICD-10-CM diagnosis code “tree” and temporally within post-vaccination follow-up. The conditional self-controlled tree-temporal scan statistic was used, conditioning on total number of cases of each diagnosis and total number of cases of any diagnosis occurring during the scanning risk window across the entire tree. P = 0.01 was the pre-specified cut-off for statistical significance.ResultsAnalysis included 352,509 doses of Moderna and 979,189 doses of Pfizer-BioNTech bivalent vaccines. After Moderna vaccination, no statistically significant clusters were found. After Pfizer-BioNTech, there were clusters of unspecified adverse events (Days 1–3, p = 0.0001–0.0007), influenza (Days 35–56, p = 0.0001), cough (Days 44–55, p = 0.0002), and COVID-19 (Days 52–56, p = 0.0004).ConclusionsFor Pfizer-BioNTech only, we detected clusters of: (1) unspecified adverse effects, as have been observed in other vaccine studies using this method, and (2) respiratory disease toward the end of follow-up. The respiratory clusters were likely due to overlap of follow-up with the spread of respiratory syncytial virus, influenza, and COVID-19, i.e., confounding by seasonality. The untargeted nature of the method and its inherent adjustment for the many diagnoses and risk intervals evaluated are unique advantages. Limitations include susceptibility to time-varying confounding, lower statistical power for assessing risks of specific outcomes than in traditional studies targeting fewer outcomes, and the possibility of missing adverse events not strongly clustered in time or within the “tree.” |
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| Keywords: | Covid-19 Data-mining Epidemiologic research design Vaccination Vaccines |
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