Prospective influenza vaccine safety surveillance using fresh data in the Sentinel System

Assessment of potential adverse events following the 2022-2023 seasonal influenza vaccines among U.S. adults aged 65 years and older

BACKGROUND

While safety of influenza vaccines is well-established, some studies have suggested potential associations between influenza vaccines and certain adverse events (AEs). This study examined the safety of the 2022-2023 influenza vaccines among U.S. adults ≥ 65 years.

METHODS

A self-controlled case series compared incidence rates of anaphylaxis, encephalitis/encephalomyelitis, Guillain-Barré Syndrome (GBS), and transverse myelitis following 2022-2023 seasonal influenza vaccinations (i.e., any, high-dose or adjuvanted) in risk and control intervals among Medicare beneficiaries ≥ 65 years. We used conditional Poisson regression to estimate incidence rate ratios (IRRs) and 95 % confidence intervals (CIs) adjusted for event-dependent observation time and seasonality. Analyses also accounted for uncertainty from outcome misclassification where feasible. For AEs with any statistically significant associations, we stratified results by concomitant vaccination status.

RESULTS

Among 12.7 million vaccine recipients, we observed 76 anaphylaxis, 276 encephalitis/encephalomyelitis, 134 GBS and 75 transverse myelitis cases. Only rates of anaphylaxis were elevated in risk compared to control intervals. With all adjustments, an elevated, but non-statistically significant, anaphylaxis rate was observed following any (IRR: 2.40, 95% CI: 0.96-6.03), high-dose (IRR: 2.31, 95% CI: 0.67-7.91), and adjuvanted (IRR: 3.28, 95% CI: 0.71-15.08) influenza vaccination; anaphylaxis IRRs were 2.54 (95% CI: 0.49-13.05) and 1.64 (95% CI: 0.38-7.05) for persons with and without concomitant vaccination, respectively.

CONCLUSIONS

Rates of encephalitis/encephalomyelitis, GBS, or transverse myelitis were not elevated following 2022-2023 seasonal influenza vaccinations among U.S. adults ≥ 65 years. There was an increased rate of anaphylaxis following influenza vaccination that may have been influenced by concomitant vaccination.

Active surveillance for adverse events of influenza vaccine safety in elderly cancer patients using self-controlled tree-temporal scan statistic analysis

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.

Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions

While vaccines are rigorously tested for safety and efficacy in clinical trials, these trials do not include enough subjects to detect rare adverse events, and they generally exclude special populations such as pregnant women. It is therefore necessary to conduct postmarketing vaccine safety assessments using observational data sources. The study of rare events has been enabled in through large linked databases and distributed data networks, in combination with development of case-centred methods. Distributed data networks necessitate common protocols, definitions, data models and analytics and the processes of developing and employing these tools are rapidly evolving. Assessment of vaccine safety in pregnancy is complicated by physiological changes, the challenges of mother-child linkage and the need for long-term infant follow-up. Potential sources of bias including differential access to and utilisation of antenatal care, immortal time bias, seasonal timing of pregnancy and unmeasured determinants of pregnancy outcomes have yet to be fully explored. Available tools for assessment of evidence generated in postmarketing studies may downgrade evidence from observational data and prioritise evidence from randomised controlled trials. However, real-world evidence based on real-world data is increasingly being used for safety assessments, and new tools for evaluating real-world evidence have been developed. The future of vaccine safety surveillance, particularly for rare events and in special populations, comprises the use of big data in single countries as well as in collaborative networks. This move towards the use of real-world data requires continued development of methodologies to generate and assess real world evidence.

Exact sequential test for clinical trials and post-market drug and vaccine safety surveillance with Poisson and binary data

In sequential analysis, hypothesis testing is performed repeatedly in a prospective manner as data accrue over time to quickly arrive at an accurate conclusion or decision. In this tutorial paper, detailed explanations are given for both designing and operating sequential testing. We describe the calculation of exact thresholds for stopping or signaling, statistical power, expected time to signal, and expected sample sizes for sequential analysis with Poisson and binary type data. The calculations are run using the package Sequential, constructed in R language. Real data examples are inspired on clinical trials practice, such as the current efforts to develop treatments to face the COVID-19 pandemic, and the comparison of treatments of osteoporosis. In addition, we mimic the monitoring of adverse events following influenza vaccination and Pediarix vaccination.

A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System

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.

Diagnosis and management of hypersensitivity reactions to vaccines

INTRODUCTION

Many countries in Europe now recommend and enforce mandatory vaccinations to improve vaccination coverage. Thus, the number of adverse events following immunization (AEFI) may show an increase. Among these events, severe hypersensitivity reactions to vaccines are rare. However, it is important that they be identified and recognized so that they may be adequately managed.

AREAS COVERED

The literature search was undertaken through PubMed and Embase to identify English-language papers focusing on hypersensitivity to vaccines.

EXPERT OPINION

Hypersensitivity reactions following vaccinations are rare and are classified according to their chronology and extension: immediate when they occur within the first 4 hours following administration and non-immediate when they occur later. Local reactions are the most common adverse event following injection of vaccines and generally do not require any allergy workup. Immediate reactions, however, are potentially IgE-mediated and require an allergy workup. In general, a previously known food allergy (i.e., egg or milk) is not a contraindication to immunizations. Patients with a known allergy to gelatin, yeast, latex, antibiotics, or other specific components of vaccines require an allergy workup before administration of the vaccine.

The risk of febrile seizures following influenza and 13-valent pneumococcal conjugate vaccines

BACKGROUND

Evidence on the risk of febrile seizures after inactivated influenza vaccine (IIV) and 13-valent pneumococcal conjugate vaccine (PCV13) is mixed. In the FDA-sponsored Sentinel Initiative, we examined risk of febrile seizures after IIV and PCV13 in children 6-23 months of age during the 2013-14 and 2014-15 influenza seasons.

METHODS

Using claims data and a self-controlled risk interval design, we compared the febrile seizure rate in a risk interval (0-1 days) versus control interval (14-20 days). In exploratory analyses, we assessed whether the effect of IIV was modified by concomitant PCV13 administration.

RESULTS

Adjusted for age, calendar time and concomitant administration of the other vaccine, the incidence rate ratio (IRR) for risk of febrile seizures following IIV was 1.12 (95% CI 0.80, 1.56) and following PCV13 was 1.80 (95% CI 1.29, 2.52). The attributable risk for febrile seizures following PCV13 ranged from 0.33 to 5.16 per 100,000 doses by week of age. The age and calendar-time adjusted IRR comparing exposed to unexposed time was numerically larger for concomitant IIV and PCV13 (IRR 2.80, 95% CI 1.63, 4.83), as compared to PCV13 without concomitant IIV (IRR 1.54, 95% CI 1.04, 2.28), and the IRR for IIV without concomitant PCV13 suggested no independent effects of IIV (IRR 0.94, 95% CI 0.63, 1.42). Taken together, this suggests a possible interaction between IIV and PCV13, though our study was not sufficiently powered to provide a precise estimate of the interaction.

CONCLUSIONS

We found an elevated risk of febrile seizures after PCV13 vaccine but not after IIV. The risk of febrile seizures after PCV13 is low compared to the overall risk in this population of children, and the risk should be interpreted in the context of the importance of preventing pneumococcal infections.

Cardiovascular safety of linagliptin compared with other oral glucose-lowering agents in patients with type 2 diabetes: A sequential monitoring programme in routine care

AIM

To evaluate the safety of linagliptin versus other glucose-lowering medications in a multi-year monitoring programme using insurance claims data.

METHODS

In two commercial US claims databases, we identified three pairwise 1:1 propensity-score (PS)-matched cohorts of patients with type 2 diabetes (T2D) aged ≥18 years initiating linagliptin or a comparator (other dipeptidyl peptidase-4 [DPP-4] inhibitors [n = 31 492 pairs], pioglitazone [n = 23 316 pairs], or second-generation sulphonylureas [n = 19 731 pairs]) between May 2011 and December 2015. The primary endpoint was the risk of a composite cardiovascular (CV) outcome (hospitalization for myocardial infarction, stroke, unstable angina, or coronary revascularization). We estimated pooled hazard ratios (HRs) and 95% confidence intervals (CIs), controlling for >100 baseline characteristics.

RESULTS

Patient characteristics were well balanced after PS-matching. The mean age was 55 years and mean follow-up was 0.8 years. Linagliptin conferred a similar risk of the composite CV outcome compared to other DPP-4 inhibitors (HR 0.91, 95% CI 0.79-1.05) and pioglitazone (HR 0.98, 95% CI 0.84-1.15), and showed a reduced risk of CV outcomes compared to second-generation sulphonylureas (HR 0.76, 95% CI 0.64--0.92). Key findings were signalled at the first interim analysis in June 2013 and solidified during ongoing monitoring until 2015.

CONCLUSION

Analyses from a large monitoring programme in routine care of patients with T2D, showed that linagliptin had similar CV safety compared to other DPP-4 inhibitors and pioglitazone, and a reduced CV risk compared to sulphonylureas.

Alpha spending for historical versus surveillance Poisson data with CMaxSPRT

Sequential analysis hypothesis testing is now an important tool for postmarket drug and vaccine safety surveillance. When the number of adverse events accruing in time is assumed to follow a Poisson distribution, and if the baseline Poisson rate is assessed only with uncertainty, the conditional maximized sequential probability ratio test, CMaxSPRT, is a formal solution. CMaxSPRT is based on comparing monitored data with historical matched data, and it was primarily developed under a flat signaling threshold. This paper demonstrates that CMaxSPRT can be performed under nonflat thresholds too. We pose the discussion in the light of the alpha spending approach. In addition, we offer a rule of thumb for establishing the best shape of the signaling threshold in the sense of minimizing expected time to signal and expected sample size. An example involving surveillance for adverse events after influenza vaccination is used to illustrate the method.

Updates on Influenza Vaccination in Children

Influenza vaccination is recommended for all children 6 months of age and older who do not have contraindications. This article provides an overview of information concerning burden of influenza among children in the United States; US-licensed influenza vaccines; vaccine immunogenicity, effectiveness, and safety; and recent updates relevant to use of these vaccines in pediatric populations. Influenza antiviral medications are discussed. Details concerning vaccine-related topics may be found in the current US Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices recommendations for use of influenza vaccines (https://www.cdc.gov/vaccines/hcp/acip-recs/vacc-specific/flu.html). Additional information on influenza antivirals is located at https://www.cdc.gov/flu/professionals/antivirals/index.htm.

Defining the interval for monitoring potential adverse events following immunization (AEFIs) after receipt of live viral vectored vaccines

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.

Outpatient influenza antivirals in a distributed data network for influenza surveillance

Electronic data collected from routine health care can be used for public health surveillance. To examine the Sentinel System, a distributed data network of health plans, as a source for influenza surveillance, we compared trends in outpatient prescription dispensings of influenza antivirals in Sentinel to trends in CDC's ILINet and NREVSS systems over five seasons. There were 2 102 885 dispensed prescriptions of oseltamivir capsules, 494 188 of oseltamivir powder, and 7955 of zanamivir. Across all seasons, the magnitude and timing of peaks in drug utilization were highly comparable to those in ILINet and NREVSS. Oseltamivir capsules and powder were well correlated with ILINet and NREVSS. This lays the foundation for further exploration of Sentinel's utility for influenza surveillance.

Near Real-time Surveillance for Consequences of Health Policies Using Sequential Analysis

BACKGROUND

New health policies may have intended and unintended consequences. Active surveillance of population-level data may provide initial signals of policy effects for further rigorous evaluation soon after policy implementation.

OBJECTIVE

This study evaluated the utility of sequential analysis for prospectively assessing signals of health policy impacts. As a policy example, we studied the consequences of the widely publicized Food and Drug Administration's warnings cautioning that antidepressant use could increase suicidal risk in youth.

METHOD

This was a retrospective, longitudinal study, modeling prospective surveillance, using the maximized sequential probability ratio test. We used historical data (2000-2010) from 11 health systems in the US Mental Health Research Network. The study cohort included adolescents (ages 10-17 y) and young adults (ages 18-29 y), who were targeted by the warnings, and adults (ages 30-64 y) as a comparison group. Outcome measures were observed and expected events of 2 possible unintended policy outcomes: psychotropic drug poisonings (as a proxy for suicide attempts) and completed suicides.

RESULTS

We detected statistically significant (P<0.05) signals of excess risk for suicidal behavior in adolescents and young adults within 5-7 quarters of the warnings. The excess risk in psychotropic drug poisonings was consistent with results from a previous, more rigorous interrupted time series analysis but use of the maximized sequential probability ratio test method allows timely detection. While we also detected signals of increased risk of completed suicide in these younger age groups, on its own it should not be taken as conclusive evidence that the policy caused the signal. A statistical signal indicates the need for further scrutiny using rigorous quasi-experimental studies to investigate the possibility of a cause-and-effect relationship.

CONCLUSIONS

This was a proof-of-concept study. Prospective, periodic evaluation of administrative health care data using sequential analysis can provide timely population-based signals of effects of health policies. This method may be useful to use as new policies are introduced.

Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network

PURPOSE OF REVIEW

An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative.

RECENT FINDINGS

These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size.

SUMMARY

Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.

Innovative Digital Tools and Surveillance Systems for the Timely Detection of Adverse Events at the Point of Care: A Proof-of-Concept Study

INTRODUCTION AND OBJECTIVE

Regulatory authorities often receive poorly structured safety reports requiring considerable effort to investigate potential adverse events post hoc. Automated question-and-answer systems may help to improve the overall quality of safety information transmitted to pharmacovigilance agencies. This paper explores the use of the VACC-Tool (ViVI Automated Case Classification Tool) 2.0, a mobile application enabling physicians to classify clinical cases according to 14 pre-defined case definitions for neuroinflammatory adverse events (NIAE) and in full compliance with data standards issued by the Clinical Data Interchange Standards Consortium.

METHODS

The validation of the VACC-Tool 2.0 (beta-version) was conducted in the context of a unique quality management program for children with suspected NIAE in collaboration with the Robert Koch Institute in Berlin, Germany. The VACC-Tool was used for instant case classification and for longitudinal follow-up throughout the course of hospitalization. Results were compared to International Classification of Diseases , Tenth Revision (ICD-10) codes assigned in the emergency department (ED).

RESULTS

From 07/2013 to 10/2014, a total of 34,368 patients were seen in the ED, and 5243 patients were hospitalized; 243 of these were admitted for suspected NIAE (mean age: 8.5 years), thus participating in the quality management program. Using the VACC-Tool in the ED, 209 cases were classified successfully, 69 % of which had been missed or miscoded in the ED reports. Longitudinal follow-up with the VACC-Tool identified additional NIAE.

CONCLUSION

Mobile applications are taking data standards to the point of care, enabling clinicians to ascertain potential adverse events in the ED setting and during inpatient follow-up. Compliance with Clinical Data Interchange Standards Consortium (CDISC) data standards facilitates data interoperability according to regulatory requirements.

Advancing Public Health Using Regulatory Science to Enhance Development and Regulation of Medical Products: Food and Drug Administration Research at the Center for Biologics Evaluation and Research

Center for Biologics Evaluation and Research enhances and supports regulatory decision-making and policy development. This work contributes to our regulatory mission, advances medical product development, and supports Food and Drug Administration’s regulatory response to public health crises. This review presents some examples of our diverse scientific work undertaken in recent years to support our regulatory and public health mission.

Vaccine Case-Population: A New Method for Vaccine Safety Surveillance

INTRODUCTION

The case-population approach compares exposure among cases to that of their source population. By using aggregated data to estimate the denominator, this approach can provide a real-time estimate of an association that could be particularly valuable to explore urgent vaccine safety concerns and to generate signals during a vaccine campaign.

OBJECTIVE

Our objective was to present the vaccine case-population method, a method derived from the case-population approach and adapted for vaccine safety surveillance, and to test it using several published examples.

METHODS

For the vaccine case-population method, exposure in the population is estimated from the sum of at-risk periods using the number of vaccinated individuals, or data of vaccine sales, and the at-risk period considered for the vaccine-event pair. The vaccine case-population method was applied to data from published case-control studies retrieved from the MEDLINE database and having quantified risks associated with vaccines. Odds ratios derived from the vaccine case-population method were compared with those from published case-control studies.

RESULTS

A total of 20 vaccine-event pairs were retrieved in which the vaccine case-population method could be applied. For all identified vaccine-event pairs, when a significant association was found using the vaccine case-population method, a significant association was also found in the corresponding case-control study. Conversely, when no association was found by the vaccine case-population method, no association was found in the corresponding case-control study.

CONCLUSION

These results suggest that the vaccine case-population method can produce coherent conclusions and may be used in the future for prospective investigation of urgent vaccine safety concerns or for the prospective generation of vaccine safety signals. This method could also be used to identify selection bias from cases excluded from the case-control study.

Post licensure surveillance of influenza vaccines in the Vaccine Safety Datalink in the 2013-2014 and 2014-2015 seasons

PURPOSE

The changes in each year in influenza vaccine antigenic components as well as vaccine administration patterns may pose new risks of adverse events following immunization (AEs). To evaluate the safety of influenza vaccines annually administered to people ≥ 6 months, we conducted weekly post licensure surveillance for seven pre-specified adverse events following receipt of influenza vaccines during the 2013-2014 and 2014-2015 seasons in the Vaccine Safety Datalink (VSD).

METHODS

We used both a historically-controlled cohort design with the Poisson-based maximized sequential probability ratio test (maxSPRT) and a self-controlled risk interval (SCRI) design with the binomial-based maxSPRT. For each adverse event outcome, we defined the risk interval on the basis of biologic plausibility and prior literature. For the historical cohort design, numbers of expected adverse events were calculated from the prior seven seasons, adjusted for age and site. For the SCRI design, a comparison window was defined either before vaccination or after vaccination, depending on each specific outcome.

RESULTS

An elevated risk of febrile seizures 0-1 days following trivalent inactivated influenza vaccine (IIV3) was identified in children aged 6-23 months during the 2014-2015 season using the SCRI design. We found the relative risk (RR) of febrile seizures following concomitant administration of IIV3 and PCV13 was 5.3 with a 95% CI 1.87-14.75. Without concomitant PCV 13 administration, the estimated risk decreased and was no longer statistically significant (RR: 1.4; CI: 0.54 - 3.61).

CONCLUSION

No increased risks, other than for febrile seizures, were identified in influenza vaccine safety surveillance during 2013-2014 and 2014-2015 seasons in the VSD. Copyright © 2016 John Wiley & Sons, Ltd.

Uptake of new drugs in the early post-approval period in the Mini-Sentinel distributed database

PURPOSE

Several factors limit the statistical power of drug safety surveillance during the early post-approval period, including uptake of the drug and lag in data availability. This study characterized new drug uptake in the Mini-Sentinel Distributed Database and determined statistical power to detect levels of risk in post-launch safety assessments.

METHODS

The cumulative exposure among initiators of 46 new molecular entities approved from 2008 to 2011 was assessed. Using a Poisson estimation method, minimum incidence rate ratios (IRRs) detectable, with 80% power, were calculated under varying background incidence rates.

RESULTS

Twelve products (26.1%) had more than 15 000 new users after 2 years. With comparator group incidence rate of 1/1000 person-years, 16 (33.3%) products had enough exposure to detect an IRR of 5 with 24 months of data collected that would be available for assessment at 33 months post-launch. With an incidence rate of 5/1000 person-years, 23 (50%) products had enough exposure to detect an IRR of ≥3 with 2 years of data collected. At 33 months post-launch, only two (4.3%) of the drugs examined had enough data availability to detect IRR of <2, and eight (17.4%) of <3, with a background rate of 1/1000 person-years.

CONCLUSION

This study highlights the importance of drug uptake and data availability in early post-approval drug safety surveillance in Mini-Sentinel. There is limited ability to detect rate ratios below three for events with background rates of 1/1000 person-years or lower. This is largely due to low product uptake. Copyright © 2016 John Wiley & Sons, Ltd.

Prospective influenza vaccine safety surveillance using fresh data in the Sentinel System

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.