Principles of risk decision-making.

Risk management decisions in public health require consideration of a number of complex, often conflicting factors. The aim of this review was to propose a set of 10 fundamental principles to guide risk decision-making. Although each of these principles is sound in its own right, the guidance provided by different principles might lead the decision-maker in different directions. For example, where the precautionary principle advocates for preemptive risk management action under situations of scientific uncertainty and potentially catastrophic consequences, the principle of risk-based decision-making encourages decision-makers to focus on established and modifiable risks, where a return on the investment in risk management is all but guaranteed in the near term. To evaluate the applicability of the 10 principles in practice, one needs to consider 10 diverse risk issues of broad concern and explore which of these principles are most appropriate in different contexts. The 10 principles presented here afford substantive insight into the process of risk management decision-making, although decision-makers will ultimately need to exercise judgment in reaching appropriate risk decisions, accounting for all of the scientific and extra-scientific factors relevant to the risk decision at hand.

Characterization of test positivity among patients with coronavirus disease 2019 (COVID-19) in three electronic health records databases, February-November 2020.

BACKGROUND: Monitoring COVID-19 testing volumes and test positivity is an integral part of the response to the pandemic. We described the characteristics of individuals who were tested and tested positive for SARS-CoV-2 during the pre-vaccine phase of the pandemic in the United States (U.S.). METHODS: This descriptive study analyzed three U.S. electronic health record (EHR) databases (Explorys, Academic Health System, and OneFlorida) between February and November 2020, identifying patients who received an interpretable nucleic acid amplification test (NAAT) result. Test-level data were used to characterize the settings in which tests were administered. Patient-level data were used to calculate test positivity rates and characterize the demographics, comorbidities, and hospitalization rates of COVID-19-positive patients. RESULTS: Over 40% of tests were conducted in outpatient care settings, with a median time between test order and result of 0-1 day for most settings. Patients tested were mostly female (55.6-57.7%), 18-44 years of age (33.9-41.2%), and Caucasian (44.0-66.7%). The overall test positivity rate was 13.0% in Explorys, 8.0% in Academic Health System, and 8.9% in OneFlorida. The proportion of patients hospitalized within 14 days of a positive COVID-19 NAAT result was 24.2-33.1% across databases, with patients over 75 years demonstrating the highest hospitalization rates (46.7-69.7% of positive tests). CONCLUSIONS: This analysis of COVID-19 testing volume and positivity patterns across three large EHR databases provides insight into the characteristics of COVID-19-tested, COVID-19-test-positive, and hospitalized COVID-19-test-positive patients during the early phase of the pandemic in the U.S.

Development of Interoperable Computable Phenotype Algorithms for Adverse Events of Special Interest to Be Used for Biologics Safety Surveillance: Validation Study.

BACKGROUND: Adverse events associated with vaccination have been evaluated by epidemiological studies and more recently have gained additional attention with the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19. OBJECTIVE: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the Food and Drug Administration's postmarket surveillance capabilities while minimizing public burden. This study aimed to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify 5 AESIs being monitored by the Center for Disease Control and Prevention for COVID-19 or other vaccines: anaphylaxis, Guillain-Barré syndrome, myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome, and febrile seizure. This study examined whether these phenotypes have sufficiently high positive predictive value (PPV) to ensure that the cases selected for surveillance are reasonably likely to be a postbiologic adverse event. This allows patient privacy, and security concerns for the data sharing of patients who had nonadverse events can be properly accounted for when evaluating the cost-benefit aspect of our approach. METHODS: AESI phenotype algorithms were developed to apply to electronic health record data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians. To validate the performance of the algorithms, we applied them to electronic health record data from a US academic health system and provided a sample of cases for clinicians to evaluate. Performance was assessed using PPV. RESULTS: With a PPV of 93.3%, our anaphylaxis algorithm performed the best. The PPVs for our febrile seizure, myocarditis/pericarditis, thrombocytopenia syndrome, and Guillain-Barré syndrome algorithms were 89%, 83.5%, 70.2%, and 47.2%, respectively. CONCLUSIONS: Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI postmarket detection.

Development and Validation of an Algorithm for Thrombosis with Thrombocytopenia Syndrome (TTS) at Unusual Sites.

Introduction: Thrombosis with thrombocytopenia syndrome (TTS) has been reported following receipt of adenoviral vector-based COVID-19 vaccines. However, no validation studies evaluating the accuracy of International Classification of Diseases-10-Clinical Modification (ICD-10-CM)-based algorithm for unusual site TTS are available in the published literature. Methods: The purpose of this study was to assess the performance of clinical coding to 1) leverage literature review and clinical input to develop an ICD-10-CM-based algorithm to identify unusual site TTS as a composite outcome and 2) validate the algorithm against the Brighton Collaboration's interim case definition using laboratory, pathology, and imaging reports in an academic health network electronic health record (EHR) within the US Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative. Validation of up to 50 cases per thrombosis site was conducted, with positive predictive values (PPV) and 95% confidence intervals (95% CI) calculated using pathology or imaging results as the gold standard. Results: The algorithm identified 278 unusual site TTS cases, of which 117 (42.1%) were selected for validation. In both the algorithm-identified and validation cohorts, over 60% of patients were 56 years or older. The positive predictive value (PPV) for unusual site TTS was 76.1% (95% CI 67.2-83.2%) and at least 80% for all but one individual thrombosis diagnosis code. PPV for thrombocytopenia was 98.3% (95% CI 92.1-99.5%). Discussion: This study represents the first report of a validated ICD-10-CM-based algorithm for unusual site TTS. A validation effort found that the algorithm performed at an intermediate-to-high PPV, suggesting that the algorithm can be used in observational studies including active surveillance of COVID-19 vaccines and other medical products.

Characterization of COVID-19 Hospitalized Patients in Three United States Electronic Health Record Databases.

COVID-19 infections have contributed to substantial increases in hospitalizations. This study describes demographics, baseline clinical characteristics and treatments, and clinical outcomes among U.S. patients admitted to hospitals with COVID-19 during the prevaccine phase of the pandemic. A total of 20,446 hospitalized patients with a positive COVID-19 nucleic acid amplification test were identified from three large electronic health record databases during 5 February-30 November 2020 (Academic Health System: n = 4504; Explorys; n = 7492; OneFlorida: n = 8450). Over 90% of patients were ≥30 years of age, with an even distribution between sexes. At least one comorbidity was recorded in 84.6-96.1% of patients; cardiovascular and respiratory conditions (28.8-50.3%) and diabetes (25.6-44.4%) were most common. Anticoagulants were the most frequently reported medications on or up to 28 days after admission (44.5-81.7%). Remdesivir was administered to 14.1-24.6% of patients and increased over time. Patients exhibited higher COVID-19 severity 14 days following admission than the 14 days prior to and on admission. The length of in-patient hospital stay ranged from a median of 4 to 6 days, and over 85% of patients were discharged alive. These results promote understanding of the clinical characteristics and hospital-resource utilization associated with hospitalized COVID-19 over time.

Regulatory and HTA Considerations for Development of Real-World Data Derived External Controls.

Regulators and Health Technology Assessment (HTA) bodies are increasingly familiar with, and publishing guidance on, external controls derived from real-world data (RWD) to generate real-world evidence (RWE). We recently conducted a systematic literature review (SLR) evaluating publicly available information on the use of RWD-derived external controls to contextualize outcomes from uncontrolled trials submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), and/or select HTA bodies. The review identified several key operational and methodological aspects for which more detailed guidance and alignment within and between regulatory agencies and HTA bodies is necessary. This paper builds on the SLR findings by delineating a set of key takeaways for the responsible generation of fit-for-purpose RWE. Practical methodological and operational guidelines for designing, conducting, and reporting RWD-derived external control studies are explored and discussed. These considerations include: (i) early engagement with regulators and HTA bodies during the study planning phase; (ii) consideration of the appropriateness and comparability of external controls across multiple dimensions, including eligibility criteria, temporality, population representation, and clinical evaluation; (iii) ensuring adequate sample sizes, including hypothesis testing considerations; (iv) implementation of a clear and transparent strategy for assessing and addressing data quality, including data missingness across trials and RWD; (v) selection of comparable and meaningful endpoints that are operationalized and analyzed using appropriate analytic methods; and (vi) conduct of sensitivity analyses to assess the robustness of findings in the context of uncertainty and sources of potential bias.

Effectively Leveraging RWD for External Controls: A Systematic Literature Review of Regulatory and HTA Decisions.

Real-world data (RWD)-derived external controls can be used to contextualize efficacy findings for investigational therapies evaluated in uncontrolled trials. As the number of submissions to regulatory and health technology assessment (HTA) bodies using external controls rises, and in light of recent regulatory and HTA guidance on the appropriate use of RWD, there is a need to address the operational and methodological challenges impeding the quality of real-world evidence (RWE) generation and the consistency in evaluation of RWE across agencies. This systematic review summarizes publicly available information on the use of external controls to contextualize outcomes from uncontrolled trials for all indications from January 1, 2015, through August 20, 2021, that were submitted to the European Medicines Agency, the US Food and Drug Administration, and/or select major HTA bodies (National Institute for Health and Care Excellence (NICE), Haute Autorité de Santé (HAS), Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG), and Gemeinsamer Bundesausschuss (G-BA)). By systematically reviewing submissions to regulatory and HTA bodies in the context of recent guidance, this study provides quantitative and qualitative insights into how external control design and analytic choices may be viewed by different agencies in practice. The primary operational and methodological aspects identified for discussion include, but are not limited to, engagement of regulators and HTA bodies, approaches to handling missing data (a component of data quality), and selection of real-world endpoints. Continued collaboration and guidance to address these and other aspects will inform and assist stakeholders attempting to generate evidence using external controls.

Background rates of adverse events of special interest for COVID-19 vaccine safety monitoring in the United States, 2019-2020.

BACKGROUND: The U.S. Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative conducts active surveillance of adverse events of special interest (AESI) after COVID-19 vaccination. Historical incidence rates (IRs) of AESI are comparators to evaluate safety. METHODS: We estimated IRs of 17 AESI in six administrative claims databases from January 1, 2019, to December 11, 2020: Medicare claims for adults ≥ 65 years and commercial claims (Blue Health Intelligence®, CVS Health, HealthCore Integrated Research Database, IBM® MarketScan® Commercial Database, Optum pre-adjudicated claims) for adults < 65 years. IRs were estimated by sex, age, race/ethnicity (Medicare), and nursing home residency (Medicare) in 2019 and for specific periods in 2020. RESULTS: The study included >100 million enrollees annually. In 2019, rates of most AESI increased with age. However, compared with commercially insured adults, Medicare enrollees had lower IRs of anaphylaxis (11 vs 12-19 per 100,000 person-years), appendicitis (80 vs 117-155), and narcolepsy (38 vs 41-53). Rates were higher in males than females for most AESI across databases and varied by race/ethnicity and nursing home status (Medicare). Acute myocardial infarction (Medicare) and anaphylaxis (all databases) IRs varied by season. IRs of most AESI were lower during March-May 2020 compared with March-May 2019 but returned to pre-pandemic levels after May 2020. However, rates of Bell's palsy, Guillain-Barré syndrome, narcolepsy, and hemorrhagic/non-hemorrhagic stroke remained lower in multiple databases after May 2020, whereas some AESI (e.g., disseminated intravascular coagulation) exhibited higher rates after May 2020 compared with 2019. CONCLUSION: AESI background rates varied by database and demographics and fluctuated in March-December 2020, but most returned to pre-pandemic levels after May 2020. It is critical to standardize demographics and consider seasonal and other trends when comparing historical rates with post-vaccination AESI rates in the same database to evaluate COVID-19 vaccine safety.

Systematic review in evidence-based risk assessment.

Systematic reviews provide a structured framework for summarizing the available evidence in a comprehensive, objective, and transparent manner. They inform evidence-based guidelines in medicine, public policy, and more recently, in environmental health and toxicology. Many regulatory agencies have extended and adapted the well-established systematic review methods, initially developed for clinical studies, for their assessment needs. The use of systematic reviews to summarize evidence from existing human, animal, and mechanistic studies can reduce reliance on animal test data in risk assessment and can help avoid unnecessary duplication of animal experiments that have already been conducted. As alternative test methods can be expected to play an increasing role in human health risk assessment in the future, systematic reviews can be particularly helpful in validating these alternatives. The field of evidence-based toxicology has undergone extensive development since its first meeting in 2007 as a result of collaborative efforts among international experts and public health agencies, particularly with respect to the use of mechanistic data and evidence integration. The continued development and wider adoption of systematic review methodology can lead to better 3R implementation. As undertaking a systematic review can be a complex and lengthy process, it is important to understand the main steps involved. Key steps, along with current best practices, are described with references to guidance from organizations with expertise in evidence synthesis. Applications of systematic reviews in clinical, observational, and experimental studies are presented. Finally, software tools available to facilitate and increase the efficiency of completing a systematic review are described.

Development of an Evidence-Based Risk Assessment Framework.

Assessment of potential human health risks associated with environmental and other agents requires careful evaluation of all available and relevant evidence for the agent of interest, including both data-rich and data-poor agents. With the advent of new approach methodologies in toxicological risk assessment, guidance on integrating evidence from mul-tiple evidence streams is needed to ensure that all available data is given due consideration in both qualitative and quantitative risk assessment. The present report summarizes the discussions among academic, government, and private sector participants from North America and Europe in an international workshop convened to explore the development of an evidence-based risk assessment framework, taking into account all available evidence in an appropriate manner in order to arrive at the best possible characterization of potential human health risks and associated uncertainty. Although consensus among workshop participants was not a specific goal, there was general agreement on the key consider-ations involved in evidence-based risk assessment incorporating 21st century science into human health risk assessment. These considerations have been embodied into an overarching prototype framework for evidence integration that will be explored in more depth in a follow-up meeting.

Acute myocardial infarction: Development and application of an ICD-10-CM-based algorithm to a large U.S. healthcare claims-based database.

BACKGROUND: Healthcare administrative claims data hold value for monitoring drug safety and assessing drug effectiveness. The U.S. Food and Drug Administration Biologics Effectiveness and Safety Initiative (BEST) is expanding its analytical capacity by developing claims-based definitions-referred to as algorithms-for populations and outcomes of interest. Acute myocardial infarction (AMI) was of interest due to its potential association with select biologics and the lack of an externally validated International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) algorithm. OBJECTIVE: Develop and apply an ICD-10-CM-based algorithm in a U.S. administrative claims database to identify and characterize AMI populations. METHODS: A comprehensive literature review was conducted to identify validated AMI algorithms. Building on prior published methodology and consistent application of ICD-9-CM codes, an ICD-10-CM algorithm was developed via forward-backward mapping using General Equivalence Mappings and refined with clinical input. An AMI population was then identified in the IBM® MarketScan® Research Databases and characterized using descriptive statistics. RESULTS AND DISCUSSION: Between 2014-2017, 2.83-3.16 individuals/1,000 enrollees/year received ≥1 AMI diagnosis in any healthcare setting. The 2015 transition to ICD-10-CM did not result in a substantial change in the proportion of patients identified. Average patient age at first AMI diagnosis was 64.9 years, and 61.4% of individuals were male. Unspecified chest pain, hypertension, and coronary atherosclerosis of native coronary vessel/artery were most commonly reported within one day of AMI diagnosis. Electrocardiograms were the most common medical procedure and beta-blockers were the most commonly ordered cardiac medication in the one day before to 14 days following AMI diagnosis. The mean length of inpatient stay was 5.6 days (median 3 days; standard deviation 7.9 days). Findings from this ICD-10-CM-based AMI study were internally consistent with ICD-9-CM-based findings and externally consistent with ICD-9-CM-based studies, suggesting that this algorithm is ready for validation in future studies.

The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructured Data in Medical Records Through Natural Language Processing.

Introduction: The Food and Drug Administration Center for Biologics Evaluation and Research conducts post-market surveillance of biologic products to ensure their safety and effectiveness. Studies have found that common vaccine exposures may be missing from structured data elements of electronic health records (EHRs), instead being captured in clinical notes. This impacts monitoring of adverse events following immunizations (AEFIs). For example, COVID-19 vaccines have been regularly administered outside of traditional medical settings. We developed a natural language processing (NLP) algorithm to mine unstructured clinical notes for vaccinations not captured in structured EHR data. Methods: A random sample of 1,000 influenza vaccine administrations, representing 995 unique patients, was extracted from a large U.S. EHR database. NLP techniques were used to detect administrations from the clinical notes in the training dataset [80% (N = 797) of patients]. The algorithm was applied to the validation dataset [20% (N = 198) of patients] to assess performance. Full medical charts for 28 randomly selected administration events in the validation dataset were reviewed by clinicians. The NLP algorithm was then applied across the entire dataset (N = 995) to quantify the number of additional events identified. Results: A total of 3,199 administrations were identified in the structured data and clinical notes combined. Of these, 2,740 (85.7%) were identified in the structured data, while the NLP algorithm identified 1,183 (37.0%) administrations in clinical notes; 459 were not also captured in the structured data. This represents a 16.8% increase in the identification of vaccine administrations compared to using structured data alone. The validation of 28 vaccine administrations confirmed 27 (96.4%) as "definite" vaccine administrations; 18 (64.3%) had evidence of a vaccination event in the structured data, while 10 (35.7%) were found solely in the unstructured notes. Discussion: We demonstrated the utility of an NLP algorithm to identify vaccine administrations not captured in structured EHR data. NLP techniques have the potential to improve detection of vaccine administrations not otherwise reported without increasing the analysis burden on physicians or practitioners. Future applications could include refining estimates of vaccine coverage and detecting other exposures, population characteristics, and outcomes not reliably captured in structured EHR data.

Effectiveness of personal protective measures in reducing pandemic influenza transmission: A systematic review and meta-analysis.

The goal of this review was to examine the effectiveness of personal protective measures in preventing pandemic influenza transmission in human populations. We collected primary studies from Medline, Embase, PubMed, Cochrane Library, CINAHL and grey literature. Where appropriate, random effects meta-analyses were conducted using inverse variance statistical calculations. Meta-analyses suggest that regular hand hygiene provided a significant protective effect (OR=0.62; 95% CI 0.52-0.73; I2=0%), and facemask use provided a non-significant protective effect (OR=0.53; 95% CI 0.16-1.71; I2=48%) against 2009 pandemic influenza infection. These interventions may therefore be effective at limiting transmission during future pandemics. PROSPERO Registration: 42016039896.

National assessment of Canadian pandemic preparedness: Employing InFluNet to identify high-risk areas for inter-wave vaccine distribution.

BACKGROUND: Influenza pandemics emerge at irregular and unpredictable intervals to cause substantial health, economic and social burdens. Optimizing health-system response is vital to mitigating the consequences of future pandemics. METHODS: We developed a mathematical model to assess the preparedness of Canadian health systems to accommodate pandemic-related increases in patient demand. We identify vulnerable areas, assess the potential of inter-wave vaccination to mitigate impacts and evaluate the association between demographic and health-system characteristics in order to identify predictors of pandemic consequences. RESULTS: Modelled average attack rates were 23.7-37.2% with no intervention and 2.5-6.4% with pre-vaccination. Peak acute-care demand was 7.5-19.5% of capacity with no intervention and 0.6-2.6% with pre-vaccination. The peak ICU demand was 39.3-101.8% with no intervention and 2.9-13.3% with pre-vaccination. Total mortality was 2258-7944 with no intervention and 88-472 with pre-vaccination. Regions of Southern Ontario were identified as most vulnerable to surges in patient demand. The strongest predictors of peak acute-care demand and ICU demand were acute-care bed capacity (R = -0.8697; r2 = 0.7564) and ICU bed capacity (R = -0.8151; r2 = 0.6644), respectively. Demographic characteristics had mild associations with predicted pandemic consequences. CONCLUSION: Inter-wave vaccination provided adequate acute-care resource protection under all scenarios; ICU resource adequacy was protected under mild disease assumptions, but moderate and severe diseases caused demand to exceed expected availability in 21% and 49% of study areas, respectively. Our study informs priority vaccine distribution strategies for pandemic planning, emphasizing the need for targeted early vaccine distribution to high-risk individuals and areas.

Modelling community-control strategies to protect hospital resources during an influenza pandemic in Ottawa, Canada.

BACKGROUND: A novel influenza virus has emerged to produce a global pandemic four times in the past one hundred years, resulting in millions of infections, hospitalizations and deaths. There is substantial uncertainty about when, where and how the next influenza pandemic will occur. METHODS: We developed a novel mathematical model to chart the evolution of an influenza pandemic. We estimate the likely burden of future influenza pandemics through health and economic endpoints. An important component of this is the adequacy of existing hospital-resource capacity. Using a simulated population reflective of Ottawa, Canada, we model the potential impact of a future influenza pandemic under different combinations of pharmaceutical and non-pharmaceutical interventions. RESULTS: There was substantial variation in projected pandemic impact and outcomes across intervention scenarios. In a population of 1.2 million, the illness attack rate ranged from 8.4% (all interventions) to 54.5% (no interventions); peak acute care hospital capacity ranged from 0.2% (all interventions) to 13.8% (no interventions); peak ICU capacity ranged from 1.1% (all interventions) to 90.2% (no interventions); and mortality ranged from 11 (all interventions) to 363 deaths (no interventions). Associated estimates of economic burden ranged from CAD $115 million to over $2 billion when extended mass school closure was implemented. DISCUSSION: Children accounted for a disproportionate number of pandemic infections, particularly in household settings. Pharmaceutical interventions effectively reduced peak and total pandemic burden without affecting timing, while non-pharmaceutical measures delayed and attenuated pandemic wave progression. The timely implementation of a layered intervention bundle appeared likely to protect hospital resource adequacy in Ottawa. The adaptable nature of this model provides value in informing pandemic preparedness policy planning in situations of uncertainty, as scenarios can be updated in real time as more data become available. However-given the inherent uncertainties of model assumptions-results should be interpreted with caution.

Reviewing the History of Pandemic Influenza: Understanding Patterns of Emergence and Transmission.

For centuries, novel strains of influenza have emerged to produce human pandemics, causing widespread illness, death, and disruption. There have been four influenza pandemics in the past hundred years. During this time, globalization processes, alongside advances in medicine and epidemiology, have altered the way these pandemics are experienced. Drawing on international case studies, this paper provides a review of the impact of past influenza pandemics, while examining the evolution of our understanding of, and response to, these viruses. This review argues that pandemic influenza is in part a consequence of human development, and highlights the importance of considering outbreaks within the context of shifting global landscapes. While progress in infectious disease prevention, control, and treatment has improved our ability to respond to such outbreaks, globalization processes relating to human behaviour, demographics, and mobility have increased the threat of pandemic emergence and accelerated global disease transmission. Preparedness planning must continue to evolve to keep pace with this heightened risk. Herein, we look to the past for insights on the pandemic experience, underlining both progress and persisting challenges. However, given the uncertain timing and severity of future pandemics, we emphasize the need for flexible policies capable of responding to change as such emergencies develop.

Assessing the State of Knowledge Regarding the Effectiveness of Interventions to Contain Pandemic Influenza Transmission: A Systematic Review and Narrative Synthesis.

BACKGROUND: Influenza pandemics occur when a novel influenza strain, to which humans are immunologically naïve, emerges to cause infection and illness on a global scale. Differences in the viral properties of pandemic strains, relative to seasonal ones, can alter the effectiveness of interventions typically implemented to control seasonal influenza burden. As a result, annual control activities may not be sufficient to contain an influenza pandemic. PURPOSE: This study seeks to inform pandemic policy and planning initiatives by reviewing the effectiveness of previous interventions to reduce pandemic influenza transmission and infection. Results will inform the planning and design of more focused in-depth systematic reviews for specific types of interventions, thus providing the most comprehensive and current understanding of the potential for alternative interventions to mitigate the burden of pandemic influenza. METHODS: A systematic review and narrative synthesis of existing systematic reviews and meta-analyses examining intervention effectiveness in containing pandemic influenza transmission was conducted using information collected from five databases (PubMed, Medline, Cochrane, Embase, and Cinahl/EBSCO). Two independent reviewers conducted study screening and quality assessment, extracting data related to intervention impact and effectiveness. RESULTS AND DISCUSSION: Most included reviews were of moderate to high quality. Although the degree of statistical heterogeneity precluded meta-analysis, the present systematic review examines the wide variety of interventions that can impact influenza transmission in different ways. While it appears that pandemic influenza vaccination provides significant protection against infection, there was insufficient evidence to conclude that antiviral prophylaxis, seasonal influenza cross-protection, or a range of non-pharmaceutical strategies would provide appreciable protection when implemented in isolation. It is likely that an optimal intervention strategy will employ a combination of interventions in a layered approach, though more research is needed to substantiate this proposition. TRIAL REGISTRATION: PROSPERO 42016039803.