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Haguinet F, Tibaldi F, Dessart C, Bate A. Tree-temporal scan statistics for safety signal detection in vaccine clinical trials. Pharm Stat 2024. [PMID: 38622834 DOI: 10.1002/pst.2391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 04/17/2024]
Abstract
The evaluation of safety is critical in all clinical trials. However, the quantitative analysis of safety data in clinical trials poses statistical difficulties because of multiple potentially overlapping endpoints. Tree-temporal scan statistic approaches address this issue and have been widely employed in other data sources, but not to date in clinical trials. We evaluated the performance of three complementary scan statistical methods for routine quantitative safety signal detection: the self-controlled tree-temporal scan (SCTTS), a tree-temporal scan based on group comparison (BGTTS), and a log-rank based tree-temporal scan (LgRTTS). Each method was evaluated using data from two phase III clinical trials, and simulated data (simulation study). In the case study, the reference set was adverse events (AEs) in the Reference Safety Information of the evaluated vaccine. The SCTTS method had higher sensitivity than other methods, and after dose 1 detected 80 true positives (TP) with a positive predictive value (PPV) of 60%. The LgRTTS detected 49 TPs with 69% PPV. The BGTTS had 90% of PPV with 38 TPs. In the simulation study, with simulated reference sets of AEs, the SCTTS method had good sensitivity to detect transient effects. The LgRTTS method showed the best performance for the detection of persistent effects, with high sensitivity and expected probability of type I error. These three methods provide complementary approaches to safety signal detection in clinical trials or across clinical development programmes. All three methods formally adjust for multiple testing of large numbers of overlapping endpoints without being excessively conservative.
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Affiliation(s)
| | | | | | - Andrew Bate
- Global Safety, GSK, Middlesex, UK
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
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Jeong NY, Kim CJ, Park SM, Kim YJ, Lee J, Choi NK. Active surveillance for adverse events of influenza vaccine safety in elderly cancer patients using self-controlled tree-temporal scan statistic analysis. Sci Rep 2023; 13:13346. [PMID: 37587127 PMCID: PMC10432531 DOI: 10.1038/s41598-023-40091-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
Both cancer patients and the elderly are at high risk of developing flu complications, so influenza vaccination is recommended. We aimed to evaluate potential adverse events (AEs) following influenza vaccination in elderly cancer patients using the self-controlled tree-temporal scan statistic method. From a large linked database of Korea Disease Control and Prevention Agency vaccination data and the National Health Insurance Service claims data, we identified cancer patients aged over 65 who received flu vaccines during the 2016/2017 and 2017/2018 seasons. We included all the outcomes occurring on 1-84 days post-vaccination and evaluated all temporal risk windows, which started 1-28 days and ended 2-42 days. Patients who were diagnosed with the same disease during a year prior to vaccination were excluded. We used the hierarchy of ICD-10 to identify statistically significant clustering. This study included 431,276 doses of flu vaccine. We detected signals for 1 set: other dorsopathies on 1-15 days (attributable risk 16.5 per 100,000, P = 0.017). Dorsopathy is a known AE of influenza vaccine. No statistically significant clusters were found when analyzed by flu season. Therefore, influenza vaccination is more recommended for elderly patients with cancer and weakened immune systems.
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Affiliation(s)
- Na-Young Jeong
- Department of Health Convergence, College of Science & Industry Convergence, Ewha Womans University, Seoul, Korea
| | - Chung-Jong Kim
- Department of Internal Medicine, Ewha Womans University Seoul Hospital, Seoul, Korea
| | - Sang Min Park
- Department of Family Medicine, Seoul National University Hospital, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul National University College of Medicine, Seoul, Korea
| | - Ye-Jee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joongyub Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Nam-Kyong Choi
- Department of Health Convergence, College of Science & Industry Convergence, Ewha Womans University, Seoul, Korea.
- Graduate School of Industrial Pharmaceutical Science, College of Pharmacy, Ewha Womans University, Seoul, Korea.
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Katherine Yih W, Daley MF, Duffy J, Fireman B, McClure DL, Nelson JC, Qian L, Smith N, Vazquez-Benitez G, Weintraub E, Williams JTB, Xu S, Maro JC. Safety signal identification for COVID-19 bivalent booster vaccination using tree-based scan statistics in the Vaccine Safety Datalink. Vaccine 2023; 41:5265-5270. [PMID: 37479610 DOI: 10.1016/j.vaccine.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/27/2023] [Accepted: 07/07/2023] [Indexed: 07/23/2023]
Abstract
BACKGROUND Traditional 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. METHODS Vaccine 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. RESULTS Analysis 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). CONCLUSIONS For 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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Affiliation(s)
- W Katherine Yih
- Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States.
| | | | - Jonathan Duffy
- Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Bruce Fireman
- Kaiser Permanente Northern California, Oakland, CA, United States
| | - David L McClure
- Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Jennifer C Nelson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Lei Qian
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA, United States
| | - Ning Smith
- Kaiser Permanente Northwest, Portland, OR, United States
| | | | - Eric Weintraub
- Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | | | - Stanley Xu
- Kaiser Permanente Southern California Research and Evaluation, Pasadena, CA, United States
| | - Judith C Maro
- Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States
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Suarez EA, Nguyen M, Zhang D, Zhao Y, Stojanovic D, Munoz M, Liedtka J, Anderson A, Liu W, Dashevsky I, Cole D, DeLuccia S, Menzin T, Noble J, Maro JC. Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System. Pharmacoepidemiol Drug Saf 2023; 32:126-136. [PMID: 35871766 DOI: 10.1002/pds.5512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/14/2022] [Accepted: 07/21/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. METHODS We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. RESULTS A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. CONCLUSIONS In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.
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Affiliation(s)
- Elizabeth A Suarez
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Nguyen
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Di Zhang
- Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yueqin Zhao
- Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Danijela Stojanovic
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Monica Munoz
- Division of Pharmacovigilance, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jane Liedtka
- Division of Pediatric and Maternal Health, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Abby Anderson
- Division of Urology, Obstetrics and Gynecology, Center for Drug and Evaluation Research, US Food and Drug Administration, Beltsville, Maryland, USA
| | - Wei Liu
- Division of Epidemiology, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Inna Dashevsky
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - David Cole
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Sandra DeLuccia
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Talia Menzin
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Noble
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
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Yih WK, Kulldorff M, Dashevsky I, Maro JC. Sequential Data-Mining for Adverse Events After Recombinant Herpes Zoster Vaccination Using the Tree-Based Scan Statistic. Am J Epidemiol 2023; 192:276-282. [PMID: 36227263 DOI: 10.1093/aje/kwac176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 08/13/2022] [Accepted: 10/07/2022] [Indexed: 02/07/2023] Open
Abstract
Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1-28 compared with days 29-56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring.
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Hsieh MHC, Liang HY, Tsai CY, Tseng YT, Chao PH, Huang WI, Chen WW, Lin SJ, Lai ECC. A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data. Clin Epidemiol 2023; 15:91-107. [PMID: 36699647 PMCID: PMC9868282 DOI: 10.2147/clep.s395922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023] Open
Abstract
Purpose Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating data-mining tools, and evaluate adaptability of such system. Patients and Methods Based on relevant guidelines and structural frameworks in Taiwan's pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan's National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system. Results Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation. Conclusion The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.
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Affiliation(s)
- Miyuki Hsing-Chun Hsieh
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan,Taiwan Drug Relief Foundation (TDRF), Taipei, Taiwan
| | | | | | - Yu-Ting Tseng
- Taiwan Drug Relief Foundation (TDRF), Taipei, Taiwan
| | - Pi-Hui Chao
- Taiwan Drug Relief Foundation (TDRF), Taipei, Taiwan
| | - Wei-I Huang
- Taiwan Drug Relief Foundation (TDRF), Taipei, Taiwan
| | - Wen-Wen Chen
- Taiwan Drug Relief Foundation (TDRF), Taipei, Taiwan
| | - Swu-Jane Lin
- Department of Pharmacy Systems, Outcomes & Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA
| | - Edward Chia-Cheng Lai
- Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan,Correspondence: Edward Chia-Cheng Lai, Email
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Yih WK, Daley MF, Duffy J, Fireman B, McClure D, Nelson J, Qian L, Smith N, Vazquez-Benitez G, Weintraub E, Williams JTB, Xu S, Maro JC. A broad assessment of covid-19 vaccine safety using tree-based data-mining in the vaccine safety datalink. Vaccine 2023; 41:826-835. [PMID: 36535825 PMCID: PMC9755007 DOI: 10.1016/j.vaccine.2022.12.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/18/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Except for spontaneous reporting systems, vaccine safety monitoring generally involves pre-specifying health outcomes and post-vaccination risk windows of concern. Instead, we used tree-based data-mining to look more broadly for possible adverse events after Pfizer-BioNTech, Moderna, and Janssen COVID-19 vaccination. METHODS Vaccine Safety Datalink enrollees receiving ≥1 dose of COVID-19 vaccine in 2020-2021 were followed for 70 days after Pfizer-BioNTech or Moderna and 56 days after Janssen vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the post-vaccination follow-up period. We used the self-controlled tree-temporal scan statistic and TreeScan software. Monte Carlo simulation was used to estimate p-values; p = 0.01 was the pre-specified cut-off for statistical significance of a cluster. RESULTS There were 4.1, 2.6, and 0.4 million Pfizer-BioNTech, Moderna, and Janssen vaccinees, respectively. Clusters after Pfizer-BioNTech vaccination included: (1) unspecified adverse effects, (2) common vaccine reactions, such as fever, myalgia, and headache, (3) myocarditis/pericarditis, and (4) less specific cardiac or respiratory symptoms, all with the strongest clusters generally after Dose 2; and (5) COVID-19/viral pneumonia/sepsis/respiratory failure in the first 3 weeks after Dose 1. Moderna results were similar but without a significant myocarditis/pericarditis cluster. Further investigation suggested the fifth signal group was a manifestation of mRNA vaccine effectiveness after the first 3 weeks. Janssen vaccinees had clusters of unspecified or common vaccine reactions, gait/mobility abnormalities, and muscle weakness. The latter two were deemed to have arisen from confounding related to practices at one site. CONCLUSIONS We detected post-vaccination clusters of unspecified adverse effects, common vaccine reactions, and, for the mRNA vaccines, chest pain and palpitations, as well as myocarditis/pericarditis after Pfizer-BioNTech Dose 2. Unique advantages of this data mining are its untargeted nature and its inherent adjustment for the multiplicity of diagnoses and risk intervals scanned.
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Affiliation(s)
- W Katherine Yih
- Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States
| | | | - Jonathan Duffy
- Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Bruce Fireman
- Kaiser Permanente Northern California, Oakland, CA, United States
| | - David McClure
- Marshfield Clinic Research Institute, Marshfield, WI, United States
| | | | - Lei Qian
- Kaiser Permanente Southern California, Pasadena, CA, United States
| | - Ning Smith
- Kaiser Permanente Northwest, Portland, OR, United States
| | | | - Eric Weintraub
- Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | | | - Stanley Xu
- Kaiser Permanente Southern California, Pasadena, CA, United States
| | - Judith C Maro
- Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States
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Katherine Yih W, Daley MF, Duffy J, Fireman B, McClure D, Nelson J, Qian L, Smith N, Vazquez-Benitez G, Weintraub E, Williams JTB, Xu S, Maro JC. Tree-based data mining for safety assessment of first COVID-19 booster doses in the Vaccine Safety Datalink. Vaccine 2023; 41:460-466. [PMID: 36481108 PMCID: PMC9684100 DOI: 10.1016/j.vaccine.2022.11.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. METHODS VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. RESULTS More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10-15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1-10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. CONCLUSIONS In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.
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Affiliation(s)
- W Katherine Yih
- Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States
| | | | - Jonathan Duffy
- Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Bruce Fireman
- Kaiser Permanente Northern California, Oakland, CA, United States
| | - David McClure
- Marshfield Clinic Research Institute, Marshfield, WI, United States
| | | | - Lei Qian
- Kaiser Permanente Southern California, Pasadena, CA, United States
| | - Ning Smith
- Kaiser Permanente Northwest, Portland, OR, United States
| | | | - Eric Weintraub
- Immunization Safety Office, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | | | - Stanley Xu
- Kaiser Permanente Southern California, Pasadena, CA, United States
| | - Judith C Maro
- Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, United States
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Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs. Epidemiology 2023; 34:90-98. [PMID: 36252086 DOI: 10.1097/ede.0000000000001561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BACKGROUND Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. METHODS We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. RESULTS The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. CONCLUSIONS Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.
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Yih WK, Kulldorff M, Dashevsky I, Maro JC. A Broad Safety Assessment of the Recombinant Herpes Zoster Vaccine. Am J Epidemiol 2022; 191:957-964. [PMID: 35152283 PMCID: PMC9071519 DOI: 10.1093/aje/kwac030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 01/25/2022] [Accepted: 02/08/2022] [Indexed: 12/30/2022] Open
Abstract
The recombinant herpes zoster vaccine (RZV), approved as a 2-dose series in the United States in October 2017, has proven highly effective and generally safe. However, a small risk of Guillain-Barré syndrome after vaccination was identified after approval, and questions remain about other possible adverse events. This data-mining study assessed RZV safety in the United States using the self-controlled tree-temporal scan statistic, scanning data on thousands of diagnoses recorded during follow-up to detect any statistically unusual temporal clustering of cases within a large hierarchy of diagnoses. IBM MarketScan data on commercially insured persons at least 50 years of age receiving RZV between January 1, 2018, and May 5, 2020, were used, including 56 days of follow-up; 1,014,329 doses were included. Statistically significant clustering was found within a few days of vaccination for unspecified adverse effects, complications, or reactions to immunization or other medical substances/care; fever; unspecified allergy; syncope/collapse; cellulitis; myalgia; and dizziness/giddiness. These findings are consistent with the known safety profile of this and other injected vaccines. No cluster of Guillain-Barré syndrome was detected, possibly due to insufficient sample size. This signal-detection method has now been applied to 5 vaccines, with consistently plausible results, and seems a promising addition to vaccine-safety evaluation methods.
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Affiliation(s)
- W Katherine Yih
- Correspondence to Dr. W. Katherine Yih, Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA 02215 (e-mail: )
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Safety surveillance of varicella vaccine using tree-temporal scan analysis. Vaccine 2021; 39:6378-6384. [PMID: 34561139 DOI: 10.1016/j.vaccine.2021.09.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 08/03/2021] [Accepted: 09/13/2021] [Indexed: 11/20/2022]
Abstract
IMPORTANCE Passive surveillance systems are susceptible to the under-reporting of adverse events (AE) and a lack of information pertaining to vaccinated populations. Conventional active surveillance focuses on predefined AEs. Advanced data mining tools could be used to identify unusual clusters of potential AEs after vaccination. OBJECTIVE To assess the feasibility of a novel tree-based statistical approach to the identification of AE clustering following the implementation of a varicella vaccination program among one-year-olds. SETTING AND PARTICIPANTS This nationwide safety surveillance was based on data from the Taiwan National Health Insurance database and National Immunization Information System for the period 2004 through 2014. The study population was children aged 12-35 months who received the varicella vaccine. EXPOSURE First-dose varicella vaccine. OUTCOMES AND MEASURES All incident ICD-9-CM diagnoses (emergency or inpatient departments) occurring 1-56 days after the varicella vaccination were classified within a hierarchical system of diagnosis categories using Multi-Level Clinical Classifications Software. A self-controlled tree-temporal data mining tool was then used to explore the incidence of AE clustering with a variety of potential risk intervals. The comparison interval consisted of days in the 56-day follow-up period that fell outside the risk interval. RESULTS Among 1,194,189 varicella vaccinees with no other same-day vaccinations, nine diagnoses with clustering features were categorized into four safety signals: fever on days 1-6 (attributable risk [AR] 38.5 per 100,000, p < 0.001), gastritis and duodenitis on days 1-2 (AR 5.9 per 100,000, p < 0.001), acute upper respiratory infection on days 1-5 (AR 11.0 per 100,000, p = 0.006), and varicella infection on days 1-9 (AR 2.7 per 100,000, p < 0.001). These safety profiles and their corresponding risk intervals have been identified in previous safety surveillance studies. CONCLUSIONS Unexpected clusters of AEs were not detected after the mass administration of childhood varicella vaccines in Taiwan. The tree-temporal statistical method is a feasible approach to the safety surveillance of vaccines in populations of young children.
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Yih WK, Kulldorff M, Dashevsky I, Maro JC. A Broad Safety Assessment of the 9-Valent Human Papillomavirus Vaccine. Am J Epidemiol 2021; 190:1253-1259. [PMID: 33558897 PMCID: PMC8245868 DOI: 10.1093/aje/kwab022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/29/2021] [Accepted: 02/03/2021] [Indexed: 12/14/2022] Open
Abstract
Parents indicate that safety is their top concern about human papillomavirus (HPV) vaccination. A data-mining method not requiring prespecification of health outcome(s) or postexposure period(s) of potentially increased risk can be used to identify possible associations between an exposure and any of thousands of medically attended health outcomes; this method was applied to data on the 9-valent HPV vaccine (HPV9) to detect potential safety problems. Data on 9- to 26-year-olds who had received HPV9 vaccine between November 4, 2016, and August 5, 2018, inclusive, were extracted from the MarketScan database and analyzed for statistically significant clustering of incident diagnoses within the hierarchy of diagnoses coded using the International Classification of Diseases and temporally within the 1 year after vaccination, using the self-controlled tree-temporal scan statistic and TreeScan software. Only 56 days of postvaccination enrollment was required; subsequent follow-up was censored at disenrollment. Multiple testing was adjusted for. The analysis included 493,089 doses of HPV9. Almost all signals resulted from temporal confounding, not unexpected with a 1-year follow-up period. The only plausible signals were for nonspecific adverse events (e.g., injection-site reactions, headache) on days 1–2 after vaccination, with attributable risks as low as 1 per 100,000 vaccinees. Considering the broad scope of the evaluation and the high statistical power, the findings of no specific serious adverse events should provide reassurance about this vaccine’s safety.
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Affiliation(s)
- W Katherine Yih
- Correspondence to Dr. W. Katherine Yih, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA 02215 (e-mail: )
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13
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Wang SV, Maro JC, Gagne JJ, Patorno E, Kattinakere S, Stojanovic D, Eworuke E, Baro E, Ouellet-Hellstrom R, Nguyen M, Ma Y, Dashevsky I, Cole D, DeLuccia S, Hansbury A, Pestine E, Kulldorff M. A General Propensity Score for Signal Identification Using Tree-Based Scan Statistics. Am J Epidemiol 2021; 190:1424-1433. [PMID: 33615330 DOI: 10.1093/aje/kwab034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 12/25/2022] Open
Abstract
The tree-based scan statistic (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) is a data-mining method that adjusts for multiple testing of correlated hypotheses when screening thousands of potential adverse events for signal identification. Simulation has demonstrated the promise of TreeScan with a propensity score (PS)-matched cohort design. However, it is unclear which variables to include in a PS for applied signal identification studies to simultaneously adjust for confounding across potential outcomes. We selected 4 pairs of medications with well-understood safety profiles. For each pair, we evaluated 5 candidate PSs with different combinations of 1) predefined general covariates (comorbidity, frailty, utilization), 2) empirically selected (data-driven) covariates, and 3) covariates tailored to the drug pair. For each pair, statistical alerting patterns were similar with alternative PSs (≤11 alerts in 7,996 outcomes scanned). Inclusion of covariates tailored to exposure did not appreciably affect screening results. Inclusion of empirically selected covariates can provide better proxy coverage for confounders but can also decrease statistical power. Unlike tailored covariates, empirical and predefined general covariates can be applied "out of the box" for signal identification. The choice of PS depends on the level of concern about residual confounding versus loss of power. Potential signals should be followed by pharmacoepidemiologic assessment where confounding control is tailored to the specific outcome(s) under investigation.
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14
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Salmon DA, Lambert PH, Nohynek HM, Gee J, Parashar UD, Tate JE, Wilder-Smith A, Hartigan-Go KY, Smith PG, Zuber PLF. Novel vaccine safety issues and areas that would benefit from further research. BMJ Glob Health 2021; 6:bmjgh-2020-003814. [PMID: 34011502 PMCID: PMC8137224 DOI: 10.1136/bmjgh-2020-003814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 12/01/2020] [Accepted: 01/06/2021] [Indexed: 12/20/2022] Open
Abstract
Vaccine licensure requires a very high safety standard and vaccines routinely used are very safe. Vaccine safety monitoring prelicensure and postlicensure enables continual assessment to ensure the benefits outweigh the risks and, when safety problems arise, they are quickly identified, characterised and further problems prevented when possible. We review five vaccine safety case studies: (1) dengue vaccine and enhanced dengue disease, (2) pandemic influenza vaccine and narcolepsy, (3) rotavirus vaccine and intussusception, (4) human papillomavirus vaccine and postural orthostatic tachycardia syndrome and complex regional pain syndrome, and (5) RTS, S/adjuvant system 01 malaria vaccine and meningitis, cerebral malaria, female mortality and rebound severe malaria. These case studies were selected because they are recent and varied in the vaccine safety challenges they elucidate. Bringing these case studies together, we develop lessons learned that can be useful for addressing some of the potential safety issues that will inevitably arise with new vaccines.
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Affiliation(s)
- Daniel A Salmon
- Global Disease Epidemiology and Control, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Hanna M Nohynek
- Infectious Disease Control and Vaccinations Unit, Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Uusimaa, Finland
| | - Julianne Gee
- Division of Healthcare Quality Promotion, National Center of Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, Georgia, USA
| | - Umesh D Parashar
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, USA
| | - Jacqueline E Tate
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, CDC, Atlanta, Georgia, USA
| | | | | | - Peter G Smith
- MRC Tropical Epidemiology Group, London School of Hygiene & Tropical Medicine, London, London, UK
| | - Patrick Louis F Zuber
- Essential Medicines and Health Products, Organisation Mondiale de la Sante, Geneve, Switzerland
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15
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Liu Z, Meng R, Yang Y, Li K, Yin Z, Ren J, Shen C, Feng Z, Zhan S. Active Vaccine Safety Surveillance: Global Trends and Challenges in China. HEALTH DATA SCIENCE 2021; 2021:9851067. [PMID: 38487501 PMCID: PMC10880162 DOI: 10.34133/2021/9851067] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/03/2021] [Indexed: 03/17/2024]
Abstract
Importance. The great success in vaccine-preventable diseases has been accompanied by vaccine safety concerns. This has caused vaccine hesitancy to be the top 10 in threats to global health. The comprehensive understanding of adverse events following immunization should be entirely based on clinical trials and postapproval surveillance. It has increasingly been recognized worldwide that the active surveillance of vaccine safety should be an essential part of immunization programs due to its complementary advantages to passive surveillance and clinical trials.Highlights. In the present study, the framework of vaccine safety surveillance was summarized to illustrate the importance of active surveillance and address vaccine hesitancy or safety concerns. Then, the global progress of active surveillance systems was reviewed, mainly focusing on population-based or hospital-based active surveillance. With these successful paradigms, the practical and reliable ways to create robust and similar systems in China were discussed and presented from the perspective of available databases, methodology challenges, policy supports, and ethical considerations.Conclusion. In the inevitable trend of the global vaccine safety ecosystem, the establishment of an active surveillance system for vaccine safety in China is urgent and feasible. This process can be accelerated with the consensus and cooperation of regulatory departments, research institutions, and data owners.
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Affiliation(s)
- Zhike Liu
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China
| | - Ruogu Meng
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Yu Yang
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Keli Li
- National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zundong Yin
- National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jingtian Ren
- Center for Drug Reevaluation, National Medical Products Administration, BeijingChina
| | - Chuanyong Shen
- Center for Drug Reevaluation, National Medical Products Administration, BeijingChina
| | - Zijian Feng
- National Immunization Programme, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Siyan Zhan
- Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China
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16
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Huybrechts KF, Kulldorff M, Hernández-Díaz S, Bateman BT, Zhu Y, Mogun H, Wang SV. Active Surveillance of the Safety of Medications Used During Pregnancy. Am J Epidemiol 2021; 190:1159-1168. [PMID: 33423046 DOI: 10.1093/aje/kwaa288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 12/15/2022] Open
Abstract
The scientific community relies on postmarketing approaches to define the risk of using medications in pregnancy because information available at the time of drug approval is limited. Most studies carried out in pregnancy focus on a single outcome or selected outcomes. However, women must balance the benefit of treatment against all possible adverse effects. We aimed to apply and evaluate a tree-based scan statistic data-mining method (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) as a safety surveillance approach that allows for simultaneous evaluation of a comprehensive range of adverse pregnancy outcomes, while preserving the overall rate of false-positive alerts. We evaluated TreeScan with a cohort design and adjustment via propensity score techniques, using 2 test cases: 1) opioids and neonatal opioid withdrawal syndrome and 2) valproate and congenital malformations, implemented in pregnancy cohorts nested within the Medicaid Analytic eXtract (January 1, 2000-December 31, 2014) and the IBM MarketScan Research Database (IBM, Armonk, New York) (January 1, 2003-September 30, 2015). In both cases, we identified known safety concerns, with only 1 previously unreported alert at the preset statistical alerting threshold. This evaluation shows the promise of TreeScan-based approaches for systematic drug safety monitoring in pregnancy. A targeted screening approach followed by deeper investigation to refine understanding of potential signals will ensure that pregnant women and their physicians have access to the best available evidence to inform treatment decisions.
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17
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Zariffa N, Russek-Cohen E. Vaccines After an Emergency Use Authorization (EUA): Modern Evidence Generation Approaches. Ther Innov Regul Sci 2021; 55:866-871. [PMID: 33886112 PMCID: PMC8061154 DOI: 10.1007/s43441-021-00290-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/26/2021] [Indexed: 12/02/2022]
Abstract
Every medical product requires additional study even after regulatory approval. We highlight several lines of enquiry to advance our understanding of COVID19 vaccines post authorization: identifying key population segments warranting more study, assessment of efficacy, and of safety data, harmonization of data relating to immune response and developing mechanisms for data and knowledge sharing across countries. We show how innovative trial designs and sources from real world data play a critical role in generating evidence.
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18
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Cocoros NM, Fuller CC, Adimadhyam S, Ball R, Brown JS, Dal Pan GJ, Kluberg SA, Lo Re V, Maro JC, Nguyen M, Orr R, Paraoan D, Perlin J, Poland RE, Driscoll MR, Sands K, Toh S, Yih WK, Platt R. A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System. Pharmacoepidemiol Drug Saf 2021; 30:827-837. [PMID: 33797815 PMCID: PMC8250843 DOI: 10.1002/pds.5240] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 12/15/2022]
Abstract
The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post‐market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID‐19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID‐19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data. Early in the pandemic, Sentinel developed a multi‐pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID‐19, validated a diagnosis‐code based algorithm for identifying patients with COVID‐19 in administrative claims data, and coordinated with other national and international initiatives. Sentinel is poised to answer important questions about the natural history of COVID‐19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID‐19 prevention and treatment.
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Affiliation(s)
- Noelle M Cocoros
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Candace C Fuller
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Sruthi Adimadhyam
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Robert Ball
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jeffrey S Brown
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | | | - Sheryl A Kluberg
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Vincent Lo Re
- Division of Infectious Diseases, Department of Medicine, and Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Michael Nguyen
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Robert Orr
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Dianne Paraoan
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Russell E Poland
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.,HCA Healthcare, Nashville, Tennessee, USA
| | - Meighan Rogers Driscoll
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Kenneth Sands
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.,HCA Healthcare, Nashville, Tennessee, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - W Katherine Yih
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Richard Platt
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
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19
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Fralick M, Kulldorff M, Redelmeier D, Wang SV, Vine S, Schneeweiss S, Patorno E. A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes. ENDOCRINOLOGY DIABETES & METABOLISM 2021; 4:e00237. [PMID: 34277962 PMCID: PMC8279599 DOI: 10.1002/edm2.237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/17/2021] [Accepted: 01/23/2021] [Indexed: 12/24/2022]
Abstract
Background Clinical trials are often underpowered to detect serious but rare adverse events of a new medication. We applied a novel data mining tool to detect potential adverse events of canagliflozin, the first sodium glucose co‐transporter 2 (SGLT2 inhibitor) in the United States, using real‐world data from shortly after its market entry and before public awareness of its potential safety concerns. Methods In a U. S. commercial claims dataset (29 March 2013–30 Sept 2015), two pairwise cohorts of patients over 18 years of age with type 2 diabetes (T2D) who were newly dispensed canagliflozin or an active comparator, that is a dipeptidyl peptidase 4 inhibitor (DPP4) or a glucagon‐like peptide 1 receptor agonist (GLP1), were identified and propensity score‐matched. We used variable ratio matching with up to four people receiving a DPP4 or GLP1 for each person receiving canagliflozin. We identified potential safety signals using a hierarchical tree‐based scan statistic data mining method with the hierarchical outcome tree constructed based on international classification of disease coding. We screened for incident adverse events where there were more outcomes observed among canagliflozin vs. comparator initiators than expected by chance, after adjusting for multiple testing. Results We identified two pairwise propensity score variable ratio matched cohorts of 44,733 canagliflozin vs. 99,458 DPP4 initiators, and 55,974 canagliflozin vs. 74,727 GLP1 initiators. When we screened inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe adverse event associated with canagliflozin initiation with p < .05 in both cohorts. When outpatient diagnoses were also considered, signals for female and male genital infections emerged in both cohorts (p < .05). Conclusions and relevance In a large population‐based study, we identified known but no other adverse events associated with canagliflozin, providing reassurance on its safety among adult patients with T2D and suggesting the tree‐based scan statistic method is a useful post‐marketing safety monitoring tool for newly approved medications.
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Affiliation(s)
- Michael Fralick
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA.,Sinai Health System and the Department of Medicine University of Toronto Toronto ON Canada
| | - Martin Kulldorff
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Donald Redelmeier
- Sunnybrook Research Institute Sunnybrook Health Sciences Centre Toronto ON Canada.,ICES Sunnybrook Health Sciences Centre Toronto ON Canada
| | - Shirley V Wang
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Seanna Vine
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Elisabetta Patorno
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
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20
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Kesselheim AS, Darrow JJ, Kulldorff M, Brown BL, Mitra-Majumdar M, Lee CC, Moneer O, Avorn J. An Overview Of Vaccine Development, Approval, And Regulation, With Implications For COVID-19. Health Aff (Millwood) 2021; 40:25-32. [DOI: 10.1377/hlthaff.2020.01620] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Aaron S. Kesselheim
- Aaron S. Kesselheim is a professor of medicine and the director of the Program on Regulation, Therapeutics, and Law in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, in Boston, Massachusetts
| | - Jonathan J. Darrow
- Jonathan J. Darrow is an assistant professor of medicine in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
| | - Martin Kulldorff
- Martin Kulldorff is a professor of medicine in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
| | - Beatrice L. Brown
- Beatrice L. Brown is a research assistant in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
| | - Mayookha Mitra-Majumdar
- Mayookha Mitra-Majumdar is a research scientist in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
| | - ChangWon C. Lee
- ChangWon C. Lee is a research assistant in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
| | - Osman Moneer
- Osman Moneer is a medical student at the Yale School of Medicine, in New Haven, Connecticut
| | - Jerry Avorn
- Jerry Avorn is a professor of medicine in the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
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Bacillus Calmette–Guérin (BCG) vaccine safety surveillance in the Korea Adverse Event Reporting System using the tree-based scan statistic and conventional disproportionality-based algorithms. Vaccine 2020; 38:3702-3710. [DOI: 10.1016/j.vaccine.2020.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 04/01/2020] [Accepted: 04/03/2020] [Indexed: 12/16/2022]
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22
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Sultana J, Trifirò G. The potential role of big data in the detection of adverse drug reactions. Expert Rev Clin Pharmacol 2020; 13:201-204. [DOI: 10.1080/17512433.2020.1740086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Janet Sultana
- Department of Biomedical and Dental Sciences and Morpho-functional Imaging, University of Messina, Messina, Italy
| | - Gianluca Trifirò
- Department of Biomedical and Dental Sciences and Morpho-functional Imaging, University of Messina, Messina, Italy
- Unit of Clinical Pharmacology, A.O.U. “G. Martino”, Messina, Italy
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The science of vaccine safety: Summary of meeting at Wellcome Trust. Vaccine 2020; 38:1869-1880. [PMID: 31987690 DOI: 10.1016/j.vaccine.2020.01.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 12/18/2019] [Accepted: 01/07/2020] [Indexed: 12/11/2022]
Abstract
Vaccines are everywhere hugely successful but are also under attack. The reason for the latter is the perception by some people that vaccines are unsafe. However that may be, vaccine safety, life any other scientific subject, must be constantly studied. It was from this point of view that a meeting was organized at the Wellcome Trust in London in May 2019 to assess some aspects of vaccine safety as subjects for scientific study. The objective of the meeting was to assess what is known beyond reasonable doubt and conversely what areas need additional studies. Although the meeting could not cover all aspects of vaccine safety science, many of the most important issues were addressed by a group of about 30 experts to determine what is already known and what additional studies are merited to assess the safety of the vaccines currently in use. The meeting began with reviews of the current situation in different parts of the world, followed by reviews of specific controversial areas, including the incidence of certain conditions after vaccination and the safety of certain vaccine components. Lastly, information about the human papillomavirus vaccine was considered because its safety has been particularly challenged by vaccine opponents. The following is a summary of the meeting findings. In addition to this summary, the meeting organizers will explore opportunities to perform studies that would enlarge knowledge of vaccine safety.
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Yih WK, Kulldorff M, Dashevsky I, Maro JC. Using the Self-Controlled Tree-Temporal Scan Statistic to Assess the Safety of Live Attenuated Herpes Zoster Vaccine. Am J Epidemiol 2019; 188:1383-1388. [PMID: 31062840 DOI: 10.1093/aje/kwz104] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/22/2019] [Accepted: 04/23/2019] [Indexed: 12/13/2022] Open
Abstract
The self-controlled tree-temporal scan statistic allows detection of potential vaccine- or drug-associated adverse events without prespecifying the specific events or postexposure risk intervals of concern. It thus opens a promising new avenue for safety studies. The method has been successfully used to evaluate the safety of 2 vaccines for adolescents and young adults, but its suitability to study vaccines for older adults had not been established. The present study applied the method to assess the safety of live attenuated herpes zoster vaccination during 2011-2017 in US adults aged ≥60 years, using claims data from Truven Health MarketScan Research Databases. Counts of International Classification of Diseases diagnosis codes recorded in emergency department or hospital settings were scanned for any statistically unusual clustering within a hierarchical tree structure of diagnoses and within 42 days after vaccination. Among 1.24 million vaccinations, 4 clusters were found: cellulitis on days 1-3, nonspecific erythematous condition on days 2-4, "other complications . . ." on days 1-3, and nonspecific allergy on days 1-6. These results are consistent with local injection-site reactions and other known, generally mild, vaccine-associated adverse events and a favorable safety profile. This method might be useful for assessing the safety of other vaccines for older adults.
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Affiliation(s)
- W Katherine Yih
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Martin Kulldorff
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts
| | - Inna Dashevsky
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Judith C Maro
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
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Mahaux O, Bauchau V, Zeinoun Z, Van Holle L. Tree-based scan statistic - Application in manufacturing-related safety signal detection. Vaccine 2019; 37:49-55. [PMID: 30470642 DOI: 10.1016/j.vaccine.2018.11.044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 10/23/2018] [Accepted: 11/14/2018] [Indexed: 11/15/2022]
Abstract
BACKGROUND AND OBJECTIVES Over the last decades, medicinal regulations have been put into place and have considerably improved manufacturing practices. Nevertheless, safety issues may still arise. Using the simulation described in this manuscript, our aim is to develop adequate detection methods for manufacturing-related safety signals, especially in the context of biological products. METHODS Pharmaceutical companies record the entire batch genealogies, from seed batches over intermediates to final product (FP) batches. We constructed a hierarchical tree based on this genealogy information and linked it to the spontaneous safety data available for the FP batch numbers. The tree-based scan statistic (TBSS) was used on simulated data as a proof of concept to locate the source that may have subsequently generated an excess of specific adverse events (AEs) within the manufacturing steps, and to evaluate the method's adjustment for multiple testing. All calculations were performed with a customized program in SAS v9.2. RESULTS The TBSS generated a close to expected number of false positive signals, demonstrating that it adjusted for multiple testing. Overall, the method detected 71% of the simulated signals at the correct production step when a 6-fold increase in reports with AEs of interest (AEOI) was applied, and 31% when a 2-fold increase was applied. The relatively low detection performance may be attributed to the higher granularity associated with the lower levels of the hierarchy, leading to a lack of power and the stringent definition criteria that were applied for a true positive result. CONCLUSION As a data-mining method for manufacturing-related safety signal detection, the TBSS may provide advantages over other disproportionality analyses (using batch information) but may benefit from complementary methods (not relaying on batch information). While the method warrants further refinement, it may improve safety signal detection and contribute to improvements in the quality of manufacturing processes.
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Affiliation(s)
- Olivia Mahaux
- Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium.
| | - Vincent Bauchau
- Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium
| | - Ziad Zeinoun
- Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium
| | - Lionel Van Holle
- Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium
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Kochhar S, Excler JL, Bok K, Gurwith M, McNeil MM, Seligman SJ, Khuri-Bulos N, Klug B, Laderoute M, Robertson JS, Singh V, Chen RT. Defining the interval for monitoring potential adverse events following immunization (AEFIs) after receipt of live viral vectored vaccines. Vaccine 2018; 37:5796-5802. [PMID: 30497831 PMCID: PMC6535369 DOI: 10.1016/j.vaccine.2018.08.085] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 08/27/2018] [Indexed: 12/13/2022]
Abstract
Live viral vectors that express heterologous antigens of the target pathogen are being investigated in the development of novel vaccines against serious infectious agents like HIV and Ebola. As some live recombinant vectored vaccines may be replication-competent, a key challenge is defining the length of time for monitoring potential adverse events following immunization (AEFI) in clinical trials and epidemiologic studies. This time period must be chosen with care and based on considerations of pre-clinical and clinical trials data, biological plausibility and practical feasibility. The available options include: (1) adapting from the current relevant regulatory guidelines; (2) convening a panel of experts to review the evidence from a systematic literature search to narrow down a list of likely potential or known AEFI and establish the optimal risk window(s); and (3) conducting "near real-time" prospective monitoring for unknown clustering's of AEFI in validated large linked vaccine safety databases using Rapid Cycle Analysis for pre-specified adverse events of special interest (AESI) and Treescan to identify previously unsuspected outcomes. The risk window established by any of these options could be used along with (4) establishing a registry of clinically validated pre-specified AESI to include in case-control studies. Depending on the infrastructure, human resources and databases available in different countries, the appropriate option or combination of options can be determined by regulatory agencies and investigators.
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Affiliation(s)
- Sonali Kochhar
- Global Healthcare Consulting, New Delhi, India; Erasmus MC, University Medical Center, Rotterdam, the Netherlands; University of Washington, Seattle, USA
| | | | - Karin Bok
- National Vaccine Program Office, Office of the Assistant Secretary for Health, US Department of Health and Human Services, Washington DC, USA
| | | | - Michael M McNeil
- Immunization Safety Office, Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA
| | - Stephen J Seligman
- Department of Microbiology and Immunology, New York Medical College, NY, USA; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller University, New York, NY, USA
| | - Najwa Khuri-Bulos
- Division of Infectious Disease, Jordan University Hospital, Amman, Jordan
| | - Bettina Klug
- Division Immunology, Paul-Ehrlich-Institut, Langen, Germany
| | | | - James S Robertson
- Independent Adviser (formerly of National Institute for Biological Standards and Control), Potters Bar, UK
| | - Vidisha Singh
- Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), USA
| | - Robert T Chen
- Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (NCHHSTP), USA; Centers for Disease Control and Prevention (CDC), Atlanta, GA, USA.
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Yih WK, Maro JC, Kulldorff M. Yih et al. Respond to "Moving From Evidence to Impact for Human Papillomavirus Vaccination". Am J Epidemiol 2018; 187:1281. [PMID: 29860469 DOI: 10.1093/aje/kwy022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 01/30/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- W Katherine Yih
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Judith C Maro
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Martin Kulldorff
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, Massachusetts
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Rositch AF, Krakow M. Invited Commentary: Moving From Evidence to Impact for Human Papillomavirus Vaccination-The Critical Role of Translation and Communication in Epidemiology. Am J Epidemiol 2018; 187:1277-1280. [PMID: 29860471 DOI: 10.1093/aje/kwy024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 01/12/2018] [Indexed: 12/24/2022] Open
Abstract
In response to the accompanying article by Yih et al. (Am J Epidemiol. 2018;187(6):1269-1276), we highlight the importance of moving beyond epidemiologic research on human papillomavirus (HPV) vaccine safety to focus on translation of this strong evidence base into successful vaccine safety communication strategies to bolster vaccine uptake. The potential of the HPV vaccine to reduce cancer incidence is substantial, yet actual HPV vaccination rates in the United States are disappointingly low in comparison with other routine childhood vaccines with similar safety profiles. This is no doubt due, in part, to persistent parental safety concerns. In 2016, safety remained the second most common reason for lack of vaccination intent by parents of unvaccinated adolescents. While the strong study by Yih et al. makes use of a novel statistical method and a large medical claims database to confirm the low occurrence of adverse events following HPV vaccination observed globally, their study also highlights a key challenge for epidemiologists: translating our research findings to other public health domains, so that evidence-informed communication strategies can be used to disseminate the information in a way that is understandable and useful to the public. Moving forward, multidisciplinary research teams will be essential to ensure that our epidemiologic findings have a broad public health impact.
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Affiliation(s)
- Anne F Rositch
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Melinda Krakow
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland
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