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ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

14.2. Vaccine safety and effectiveness

14.2.1. Vaccine safety

 

14.2.1.1. General considerations

 

The ADVANCE Report on appraisal of vaccine safety methods is a comprehensive reference providing a brief description of a wide range of direct and indirect methods of vaccine risk assessment, evaluated based on nine criteria related to five domains: Effect Measure, Statistical Criteria, Timeliness, Restriction and Robustness, and Operational Criteria. It also emphasises the specificities of safety assessment for vaccines and how they differ from other medicines, evaluates study designs, discusses perspectives of different stakeholders on risk assessment, describes experiences from other projects and systems, and provides recommendations. This document is highly relevant for all the topics covered in this section on vaccine safety.

 

Specific aspects related to vaccine safety are discussed in several other documents.

  • The Report of the CIOMS/WHO Working Group on Definition and Application of Terms for Vaccine Pharmacovigilance (2012) provides definitions and explanatory notes for the terms ‘vaccine pharmacovigilance’, ‘vaccination failure’ and ‘adverse event following immunisation (AEFI)’.
  • The CIOMS Guide to Active Vaccine Safety Surveillance (2017) describes the process of determining whether active vaccine safety surveillance is necessary, more specifically in the context of resource-limited countries, and, if so, of choosing the best type of active safety surveillance and considering key implementation issues.
  • The CIOMS Guide to Vaccine Safety Communication (2018) provides an overview of strategic communication issues faced by regulators, those responsible for vaccination policies and other stakeholders in introducing current or new vaccines in populations. Building upon existing recommendations, it provides a guide for vaccine risk communication approaches.
  • The Brighton Collaboration provides resources to facilitate and harmonise collection, analysis and presentation of vaccine safety data, including case definitions specifically intended for pharmacoepidemiological research, an electronic tool to help the classification of reported signs and symptoms, template protocols, and guidelines.
  • Module 4 (Surveillance) of the e-learning training course Vaccine Safety Basics of the World Health Organization (WHO) describes pharmacovigilance principles, causality assessment procedures, surveillance systems and places safety in the context of the vaccine benefit/risk profile. For example the systematic review Maternal Influenza Vaccination and Risk for Congenital Malformations: A Systematic Review and Meta-analysis (Obstet Gynecol 2015;126(5):1075-84) on influenza vaccination in pregnancy and risk of congenital anomalies in newborns did not find an association, adding to the evidence-base in favour of influenza vaccination in pregnancy.
  • Recommendations on vaccine-specific aspects of the EU pharmacovigilance system, including on risk management, signal detection and post-authorisation safety studies (PASS) are presented in Module P.I: Vaccines for prophylaxis against infectious diseases of the Good pharmacovigilance practices (GVP).
  • A vaccine study design selection framework for the postlicensure rapid immunization safety monitoring program (Am J Epidemiol. 2015;181(8):608-18) describes and summarises, in a tabular form, strengths and weaknesses of the cohort, case-centered, risk-interval, case-control, self-controlled risk interval (SCRI), self-controlled case series (SCCS) and case-crossover designs for vaccine safety monitoring, to support decision-making.
  • The WHO Covid-19 vaccines safety surveillance manual has been developed upon recommendation and guidance of the Global Advisory Committee on Vaccine Safety (GACVS) and other experts and addresses pharmacovigilance preparedness for the launch of COVID-19 vaccines.

There is an increasing interest in the influence of genetics on safety and efficacy outcomes of vaccination. Understanding this influence may optimise the choice of vaccines and the vaccination schedule. Research in this field is illustrated by Effects of vaccines in patients with sickle cell disease: a systematic review protocol (BMJ Open 2018;8:e021140) and Adversomics: a new paradigm for vaccine safety and design (Expert Rev Vaccines. 2015 Jul; 14(7): 935–47). Vaccinomics and Adversomics in the Era of Precision Medicine: A Review Based on HBV, MMR, HPV, and COVID-19 Vaccines (J Clin Med. 2020;9(11):3561) highlights that knowledge of genetic factors modulating responses to vaccination could contribute to the evaluation of the safety and effectiveness of vaccines, including COVID-19 vaccines.

 

14.2.1.2. Signal detection and validation

 

Besides a qualitative analysis of spontaneous case reports or case series, quantitative methods such as disproportionality analyses (described in Chapter 9) and observed vs. expected (O/E) analyses are routinely employed in signal detection for vaccines. Several documents discuss the merits and review the methods of these approaches for vaccines.

 

Disproportionality analyses

 

GVP Module P.I: Vaccines for prophylaxis against infectious diseases describes issues to be considered when applying methods for disproportionality analyses for vaccines, including the choice of the comparator group and the use of stratification. Effects of stratification on data mining in the US Vaccine Adverse Event Reporting System (VAERS) (Drug Saf. 2008;31(8):667-74) demonstrates that stratification can reveal and reduce confounding and unmask some vaccine-event pairs not found by crude analyses. However, Stratification for Spontaneous Report Databases (Drug Saf. 2008;31(11):1049-52) highlights that extensive use of stratification in signal detection algorithms should be avoided as it can mask true signals. Vaccine-Based Subgroup Analysis in VigiBase: Effect on Sensitivity in Paediatric Signal Detection (Drug Saf. 2012;35(4)335-46) further examines the effects of subgroup analyses based on the relative distribution of vaccine/non-vaccine reports in paediatric ADR data. In Performance of Stratified and Subgrouped Disproportionality Analyses in Spontaneous Databases (Drug Saf. 2016;39(4):355-64), it was found that subgrouping by vaccines/non-vaccines resulted in a decrease in both precision and sensitivity in all spontaneous report databases that contributed data.

 

The article Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization (Pharmacoepidemiol Drug Saf .2013;22(5): 477–87) explores various ways of improving performance of signal detection algorithms when looking for vaccine adverse events.

 

The article Adverse events associated with pandemic influenza vaccines: comparison of the results of a follow-up study with those coming from spontaneous reporting (Vaccine 2011;29(3):519-22) reported a more complete pattern of reactions when using two complementary methods for first characterisation of the post-marketing safety profile of a new vaccine, which may impact on signal detection.

In Review of the initial post-marketing safety surveillance for the recombinant zoster vaccine (Vaccine 2020;38(18):3489-500), the time-to-onset distribution of zoster vaccine-adverse event pairs was used to generate a quantitative signal of unexpected temporal relationship.

 

Bayesian disproportionality methods have also been developed to generate disproportionality signals. In Association of Facial Paralysis With mRNA COVID-19 Vaccines: A Disproportionality Analysis Using the World Health Organization Pharmacovigilance Database (JAMA Intern Med. 2021;e212219), a potential safety signal for facial paralysis was explored using the Bayesian neural network method.

 

In Disproportionality analysis of anaphylactic reactions after vaccination with messenger RNA coronavirus disease 2019 vaccines in the United States (Ann Allergy Asthma Immunol. 2021; S1081-1206(21)00267-2) the CDC Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) system was used in conjunction with proportional reporting ratios to evaluate whether the rates of anaphylaxis cases reported in the VAERS database following administration of mRNA COVID-19 vaccines was disproportionately different from all other vaccines.

 

Observed-to-expected analyses

 

In vaccine vigilance, an O/E analysis compares the ‘observed’ number of cases of an adverse event occurring in vaccinated individuals and recorded in a data collection system (e.g. a spontaneous reporting system or an electronic health care record database) and the ‘expected’ number of cases that would have naturally occurred in the same population without vaccination, estimated from available incidence rates in a non-vaccinated population. GVP Module P.I: Vaccines for prophylaxis against infectious diseases suggests the conduct of O/E analyses for signal validation and preliminary signal evaluation when prompt decision-making is required and there is insufficient time to review a large number of individual cases. It discusses key requirements of O/E analyses: the observed number of cases detected in a passive or active surveillance system, near real-time exposure data, appropriately stratified background incidence rates calculated on a population similar to the vaccinated population (for the expected number of cases), the definition of appropriate risk periods (where there is suspicion and/or biological plausibility that there is a vaccine‐associated increased risk of experiencing the event) and sensitivity analyses around these measures. O/E analyses may require some adjustments for continuous monitoring due to inflation of type 1 error rates when multiple tests are performed. The method is further discussed in Pharmacoepidemiological considerations in observed‐to‐expected analyses for vaccines (Pharmacoepidemiol Drug Saf. 2016;25(2):215-22) and the review Near real‐time vaccine safety surveillance using electronic health records - a systematic review of the application of statistical methods (Pharmacoepidemiol Drug Saf. 2016;25(3):225-37).

 

O/E analyses require several pre-defined assumptions based on the requirements listed above. Each of these assumptions can be associated with some uncertainties. How to manage these uncertainties is also addressed in Pharmacoepidemiological considerations in observed-to-expected analyses for vaccines (Pharmacoepidemiol Drug Saf. 2016;25(2):215–22).

 

Use of population based background rates of disease to assess vaccine safety in childhood and mass immunisation in Denmark: nationwide population based cohort study (BMJ 2012;345:e5823) illustrate the importance of collecting background rates by estimating risks of coincident associations of emergency consultations, hospitalisations and outpatients consultations with vaccination. Rates of selected disease events for several countries may vary by age, sex, method of ascertainment and geography, as shown in Incidence Rates of Autoimmune Diseases in European Healthcare Databases: A Contribution of the ADVANCE Project (Drug Saf. 2021;44(3):383-95), where age-, gender-, and calendar-year stratified incidence rates of nine autoimmune diseases in seven European healthcare databases from four countries were generated to support O/E analyses of vaccines. Guillain-Barré syndrome and influenza vaccines: A meta-analysis (Vaccine 2015; 33(31):3773-8) suggests that a trend observed between different geographical areas would be consistent with a different susceptibility of developing a particular adverse reaction among different populations. In addition, comparisons with background rates may be invalid if conditions are unmasked at vaccination visits (see Human papillomavirus vaccination of adult women and risk of autoimmune and neurological diseases (J Intern Med. 2018;283(2):154-165)).

 

The article The critical role of background rates of possible adverse events in the assessment of COVID-19 vaccine safety (Vaccine 2021;39(19):2712-18) describes two key steps for the safety evaluation of COVID-19 vaccines: defining a dynamic list of Adverse Events of Special Interest (AESIs) and establishing background rates for these AESIs, and discusses tools from the Brighton Collaboration to facilitate case evaluation.

 

A protocol for generating background rates of AESIs for the monitoring of COVID-19 vaccines has been developed by the vACcine Covid-19 monitoring readinESS (ACCESS) consortium. These background rate data are publicly available on the VAC4EU website. Similarly, the FDA Best Initiative has published a protocol for Background Rates of Adverse Events of Special Interest for COVID-19 Vaccine Safety Monitoring.  

 

In Arterial events, venous thromboembolism, thrombocytopenia, and bleeding after vaccination with Oxford-AstraZeneca ChAdOx1-S in Denmark and Norway: population based cohort study (BMJ 2021;373:n1114), observed rates of events among vaccinated people were compared with expected rates, based on national age- and sex-specific rates from the general population calculated from the same databases, thereby removing a source of variability between observed and expected rates. Where this is not possible, background rates available from multiple large healthcare databases have shown to be heterogeneous, and the choice of relevant data for a given analysis should take into account differences in database and population characteristics related to different diagnosis, recording and coding practices, source populations (e.g., inclusion of patients from general practitioners and/or hospitals), healthcare systems determining reimbursement and inclusion of data in claims databases, and linkage ability (e.g., to hospital records). This is further discussed in Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: multinational network cohort study (BMJ, 2021).

 

Sequential methods

 

Sequential methods, as described in Early detection of adverse drug events within population-based health networks: application of sequential methods (Pharmacoepidemiol Drug Saf. 2007;16(12):1275-84), allow O/E analyses to be performed on a routine (e.g. weekly) basis using cumulative data with adjustment for multiplicity. Such methods are routinely used for near-real time surveillance in the Vaccine Safety Datalink (VSD) (see: Near real-time surveillance for influenza vaccine safety: proof-of-concept in the Vaccine Safety Datalink Project, Am J Epidemiol 2010;171(2):177-88). Potential issues are described in Challenges in the design and analysis of sequentially monitored postmarket safety surveillance evaluations using electronic observational health care data (Pharmacoepidemiol Drug Saf. 2012;21(S1):62-71). A review of signals detected over 3 years with these methods in the Vaccine Safety Datalink concluded that care with data quality, outcome definitions, comparison groups and duration of surveillance is required to enable detection of true safety issues while controlling error rates (Active surveillance for adverse events: the experience of the Vaccine Safety Datalink Project (Pediatrics 2011;127(S1):S54-S64)). Sequential methods are therefore considered more valid but also more complex to perform, understand and communicate to a non-expert audience.

 

A new self-controlled case series method for analyzing spontaneous reports of adverse events after vaccination (Am J Epidemiol. 2013;178(9):1496-504) extends the self-controlled case series approach to explore and quantify vaccine safety signals from spontaneous reports. It uses parametric and nonparametric versions with different assumptions to account for the specific features of the data (e.g., large amount of underreporting and variation of reporting with time since vaccination). The method should be seen as a signal strengthening approach for quickly exploring a signal based on spontaneous reports prior to a pharmacoepidemiologic study, if any. The method was used in Intussusception after Rotavirus Vaccination -- Spontaneous Reports (N Engl J Med. 2011;365:2139) and Kawasaki disease and 13-valent pneumococcal conjugate vaccination among young children: A self-controlled risk interval and cohort study with null results (PLoS Med. 2019;16(7):e100284). 

 

The tree-based scan statistic (TreeScan) is a statistical data mining method that can be used for the detection of vaccine safety signals from large health insurance claims and electronic health records (see Drug safety data mining with a tree-based scan statistic, Pharmacoepidemiol Drug Saf. 2013;22(5):517-23). A Broad Safety Assessment of the 9-Valent Human Papillomavirus Vaccine (Am J Epidemiol. 2021;kwab022) uses the self-controlled tree-temporal scan statistic, which builds on this method but does not require pre-specified outcomes or specific post-exposure risk periods, to evaluate outcomes associated with receipt of a HPV vaccine by scanning data on all diagnoses recorded to detect any clustering of cases within a large hierarchy, or “tree,” of diagnoses as well as within the follow-up period. The method requires further evaluation of its utility for routine vaccine surveillance in terms of requirements for large databases and computer resources, as well as predictive value of the signals detected.

 

14.2.1.3. Hypothesis testing safety studies

 

A complete review of study designs and methods for hypothesis-testing studies in the field of vaccine safety is included in the ADVANCE Report on appraisal of vaccine safety methods.

 

Case-only designs

 

Traditional study designs such as cohort and case-control studies (see Chapter 5.2) may be difficult to implement for vaccines in circumstances where there is high vaccine coverage in the study population, an appropriate unvaccinated group is lacking, or adequate information on covariates at the individual level is not available. Frequent sources of confounding to be considered are socioeconomic status, underlying health status and other factors influencing the probability of being vaccinated such as access to healthcare. In such situations, case-only designs (see Chapters 5.2.3 and 5.4.3) may be useful, as illustrated in Control without separate controls: evaluation of vaccine safety using case-only methods (Vaccine 2004; 22(15-16):2064-70). It concludes that properly designed and analysed epidemiological studies using only cases, especially the SCCS method, may provide stronger evidence than large cohort studies as they control for fixed individual-level confounders (such as demographics, genetics and social deprivation) and typically have similar, sometimes higher, power.

 

Several publications have compared traditional and case-only study designs for vaccine studies:

  • Epidemiological designs for vaccine safety assessment: methods and pitfalls (Biologicals 2012;40(5):389-92) used three study designs (cohort, case-control and SCCS) to illustrate issues such as correct understanding and definition of the vaccine safety question, case definition and interpretation of findings, limitations of data sources, uncontrolled confounding, and pitfalls that apply to the individual designs.

  • Comparison of epidemiologic methods for active surveillance of vaccine safety (Vaccine 2008; 26(26):3341-45) performed a simulation study to compare four designs (matched-cohort, vaccinated-only (risk interval) cohort, case-control and SCCS) in the context of vaccine safety surveillance. The cohort study design allowed for the most rapid signal detection, the least false-positive error and highest statistical power in performing sequential analysis. The authors highlight, however, that the main limitation of this simulation is the exclusion of confounding effects and the lack of chart review, which is a time and resource intensive requirement.

  • The simulation study (Four different study designs to evaluate vaccine safety were equally validated with contrasting limitations, J Clin Epidemiol. 2006; 59(8):808-818) compared four study designs (cohort, case-control, risk-interval and SCCS) with the conclusion that all the methods were valid, with contrasting strengths and weaknesses. The SCCS method, in particular, proved to be an efficient and valid alternative to the cohort method.

  • Hepatitis B vaccination and first central nervous system demyelinating events: Reanalysis of a case-control study using the self-controlled case series method. Vaccine 2007;25(31):5938-43) describes how the SCCS found similar results as the case-control study but with greater precision as it used cases without matched controls excluded from the case-control analysis. This is at the cost of the assumption that exposures are independent of earlier events. The authors recommended that, if case-control studies of vaccination and adverse events are undertaken, parallel case-series analyses should also be conducted, where appropriate.

While the SCCS is suited to secondary use of data, it may not always be appropriate in situations where primary data collection and rapid data generation are needed (e.g., a pandemic) since follow-up time needs to be accrued. In such instances, the Self-controlled Risk Interval (SCRI) method can be used to shorten the observation time (see The risk of Guillain-Barre Syndrome associated with influenza A (H1N1) 2009 monovalent vaccine and 2009-2010 seasonal influenza vaccines: Results from self-controlled analyses,

Pharmacoepidemiol. Drug Saf 2012;21(5):546-52), historical background rates can be used for an O/E analysis (see Near real-time surveillance for influenza vaccine safety: proof-of-concept in the Vaccine Safety Datalink Project. Am J Epidemiol 2010;171(2):177-88), or a classical case-control study can be performed, as in Guillain-Barré syndrome and adjuvanted pandemic influenza A (H1N1) 2009 vaccine: multinational case-control study in Europe BMJ. 2011;343:d3908).

 

Nevertheless, the SCCS design remains an adequate method to study vaccine safety, provided the main requirements of the method are taken into account (see Chapter 5.4.3). An illustrative example is shown in Bell's palsy and influenza(H1N1)pdm09 containing vaccines: A self-controlled case series (PLoS One. 2017;12(5):e0175539).

 

Cohort-event monitoring

 

Prospective cohort-event monitoring including active surveillance of vaccinated subjects using applications and/or other web-based tools has been extensively used to monitor the safety of COVID-19 vaccines, as primary data collection was the only means to rapidly identify potential safety concerns as soon as the vaccines began to be used at large scale. A definition of cohort-event monitoring is provided in The safety of medicines in public health programmes : pharmacovigilance, an essential tool (who.int) (see Chapter 6.5, Cohort event monitoring, pp 40-41). Specialist Cohort Event Monitoring studies: a new study method for risk management in pharmacovigilance (Drug Saf. 2015;38(2):153-63) discusses the rationale and features to address possible bias, and some applications of this design. The study Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study (Lancet Infect Dis 2021;S1473) examined the proportion and probability of self-reported systemic and local side-effects 8 days after one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. Such self-reported data may introduce information bias, as some participants might be more likely to report symptoms and some may drop out; however, use of an app allowed to monitor a large sample size. Adverse events following mRNA SARS-CoV-2 vaccination among U.S. nursing home residents (Vaccine 2021) prospectively monitored residents of nursing homes using electronic health record data on vaccinations and pre-specified adverse and compared to unvaccinated residents during the same time period. As immunisation campaigns expand and vaccination coverage increases, non-vaccinated comparator groups will no longer be feasible and alternative designs will need to be applied.

 

Case-coverage design

 

The case-coverage design is a type of ecological design that uses exposure information on cases and population data on vaccination coverage to serve as control. It compares odds of exposure in cases to odds of exposure in the general population, similar to the screening method used in vaccine effectiveness studies. However, this method does not control for residual confounding and may be prone to selection bias introduced by propensity to seek care (or vaccination) and awareness of possible occurrence of a specific outcome, and it does not consider underlying medical conditions, with a limited comparability between cases and controls. In addition, it requires reliable and detailed vaccine coverage data corresponding to the population from which cases are drawn to allow control of confounding by stratified analysis. An example of a vaccine safety study using a case-coverage method is Risk of narcolepsy in children and young people receiving AS03 adjuvanted pandemic A/H1N1 2009 influenza vaccine: retrospective analysis (BMJ 2013; 346:f794).

 

Generic protocols

 

The ACCESS consortium has published four Template study protocols to support the choice of design for COVID-19 vaccine safety studies. The prospective cohort-event monitoring protocol uses primary data collection to record data on suspected adverse drug reactions from vaccinated subjects, while protocols for the rapid assessment of safety concerns or the evaluation of safety signals are based on electronic health records. The protocol Rapid assessment of COVID-19 vaccines safety concerns through electronic health records- a protocol template from the ACCESS project compares the suitability of the ecological design and the unadjusted SCRI for rapid assessment by type of AESI. Similarly, the FDA BEST Initiative has published a COVID-19 Vaccine Safety Active Monitoring Protocol and a Master Protocol: Assessment of Risk of Safety Outcomes Following COVID-19 Vaccination

 

14.2.1.4. Meta-analyses

 

The guidance on conducting meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes (Annex 1 of this Guide) also applies to vaccines. A systematic review evaluating the potential for bias and the methodological quality of meta-analyses in vaccinology (Vaccine 2007; 25(52):8794-806) provides a comprehensive overview of the methodological quality and limitations of 121 meta-analyses of vaccine studies. Association between Guillain-Barré syndrome and influenza A (H1N1) 2009 monovalent inactivated vaccines in the USA: a meta-analysis (Lancet 2013;381(9876):1461-8) describes a self-controlled risk-interval design in a meta-analysis of six studies at the patient level with a reclassification of cases according to the Brighton Collaboration classification. Meta-analysis of the risk of autoimmune thyroiditis, Guillain-Barré syndrome, and inflammatory bowel disease following vaccination with AS04-adjuvanted human papillomavirus 16/18 vaccine (Pharmacoepidemiol Drug Saf. 2020;29(9):1159-67) combined data from 18 randomised controlled trials, one cluster-randomised trial, two large observational retrospective cohort studies, and one case-control study, resulting in a large sample size for these rare events.

 

14.2.1.5. Studies on vaccine safety in special populations

 

The article Vaccine safety in special populations (Hum Vaccin 2011;7(2):269-71) highlights common methodological issues that may arise in evaluating vaccine safety in special populations, especially infants and children who often differ in important ways from healthy individuals and change rapidly during the first few years of life, and elderly patients.

 

Pregnant and lactating women represent an important group to be addressed when monitoring vaccine use, and recommendations have been provided on methodological standards to be applied in vaccine studies in this population. Pregnancy registries including pregnant women can be used to assess pregnancy and neonatal outcomes (see Chapter 4.3.5). Assessing the effect of vaccine on spontaneous abortion using time-dependent covariates Cox models (Pharmacoepidemiol Drug Saf 2012;21(8):844-50) demonstrates that rates of spontaneous abortion can be severely underestimated without survival analysis techniques using time-dependent covariates to avoid immortal time bias and shows how to fit such models. Risk of miscarriage with bivalent vaccine against human papillomavirus (HPV) types 16 and 18: pooled analysis of two randomised controlled trials (BMJ 2010; 340:c712) explains methods to calculate rates of miscarriage, address the lack of knowledge of time of conception during which vaccination might confer risk and perform subgroup and sensitivity analyses. In Harmonising Immunisation Safety Assessment in Pregnancy Part I (Vaccine 2016;34 (49): 5991-6110) and Part II (Vaccine 2017;35 (48), 6469-582), the Global Alignment of Immunization Safety Assessment in pregnancy (GAIA) project has provided a selection of case definitions and guidelines for the evaluation of pregnancy outcomes following immunization. The Systematic overview of data sources for Drug Safety in pregnancy research provides an inventory of pregnancy exposure registries and alternative data sources useful to assess the safety of prenatal vaccine exposure.

 

The Guidance for design and analysis of observational studies of fetal and newborn outcomes following COVID-19 vaccination during pregnancy (Vaccine 2021;39(14):1882-86) provides useful insights on study design, data collection, and analytical issues in COVID-19 vaccine safety studies in pregnant women, and Methodologic approaches in studies using real-world data (RWD) to measure pediatric safety and effectiveness of vaccines administered to pregnant women: A scoping review (Vaccine 2021) describes the types of data sources that have been used in maternal immunisation studies, the methods to link maternal and infant data and estimate gestational age at time of maternal vaccination, and how exposure was documented.

 

Post-authorisation studies in immunocompromised subjects are often required as this population is usually not included in the clinical development of vaccines. Influenza vaccination for immunocompromised patients: systematic review and meta-analysis by etiology (J Infect Dis 2012;206(8):1250-9) illustrates the importance of performing stratified analyses by aetiology of immunocompromise and possible limitations due to residual confounding, differences within and between etiological groups and small sample size in some etiological groups. In anticipation of the design of post-authorisation vaccine effectiveness and safety studies, the study Burden of herpes zoster in 16 selected immunocompromised populations in England: a cohort study in the Clinical Practice Research Datalink 2000–2012 (BMJ Open. 2018; 8(6): e020528) illustrated the challenges of defining an immunocompromised cohort and a relevant comparator cohort when making secondary use of a primary healthcare database.

 

14.2.2. Vaccine effectiveness

 

14.2.2.1. General considerations

 

The textbook Design and Analysis of Vaccine Studies (ME Halloran, IM Longini Jr., CJ Struchiner, Ed., Springer, 2010) presents methods for vaccine effectiveness evaluation and a conceptual framework of the different effects of vaccination at the individual and population level, and includes methods for evaluating indirect, total and overall effects of vaccination in populations.

 

The article Vaccine effects and impact of vaccination programmes in post-licensure studies (Vaccine 2013;31(48):5634-42) reviews effectiveness of vaccine and of vaccination programmes methods, proposes epidemiological measures of public health impact, describes relevant methods to measure these effects and discusses the assumptions and potential biases involved.

 

A framework for research on vaccine effectiveness (Vaccine 2018;36(48):7286-93) proposes standardised definitions, considers models of vaccine failure and provides methodological considerations for different designs. This article is useful to researchers who investigate the effectiveness of vaccines and vaccination programs and why they may fail.

 

The World Health Organisation’s Evaluation of influenza vaccine effectiveness: a guide to the design and interpretation of observational studies (2017) provides an overview of methods to study the effectiveness of influenza vaccines, also relevant for other vaccines.

 

Study designs and methods for measuring vaccine effectiveness in the Post-Licensure Rapid Immunization Safety Monitoring (PRISM) program are further explained in Exploring the Feasibility of Conducting Vaccine Effectiveness Studies in Sentinel’s PRISM Program (CBER, 2018).

 

The ADVANCE Report on appraisal of vaccine safety methods, although primarily dedicated to vaccine safety methods, also offers considerations relevant for effectiveness evaluation.

 

The WHO document Evaluation of COVID-19 vaccine effectiveness provides interim best practice guidance on how to monitor COVID-19 vaccine effectiveness using observational study designs, including considerations on effectiveness evaluation in low- and middle-income countries.

 

The template protocols developed by the ACCESS consortium for effectiveness studies of COVID-19 vaccines using the cohort design and the test-negative case-control design are published on the EU PAS Register.

 

It is worth mentioning that there are few comparative effectiveness studies of vaccines, except for some head-to-head immunogenicity studies. However, comparative effectiveness methods have been used to compare vaccination schedules or vaccine formulations. For example, see: Analysis of relative effectiveness of high-dose versus standard-dose influenza vaccines using an instrumental variable method (Vaccine 2019;37(11):1484-90) and The risk of non-specific hospitalised infections following MMR vaccination given with and without inactivated vaccines in the second year of life. Comparative self-controlled case-series study in England (Vaccine 2019;37(36):5211-17).

 

Assessment of Effectiveness of 1 Dose of BNT162b2 Vaccine for SARS-CoV-2 Infection 13 to 24 Days After Immunization (JAMA Netw Open. 2021;4(6):e2115985) compared the effectiveness of the first vaccine dose between two post-immunisation periods. It is likely that further comparative studies will be conducted to compare the real-world performance of COVID-19 vaccines. Postmarketing studies: can they provide a safety net for COVID-19 vaccines in the UK? (BMJ Evid Based Med. 2020:bmjebm-2020-111507) discusses methodological and operational aspects of post-authorisation studies of COVID-19 vaccines and provides considerations on head-to-head vaccine comparisons.

 

Vaccination programmes have indirect effects at the population-level, also called herd immunity, as a result of reduced transmission. Besides measuring the direct effect of vaccination in vaccine effectiveness studies, it is important to assess whether vaccination will have an effect on transmission. As a high risk setting for transmission, households can provide early evidence of such impact. Impact of vaccination on household transmission of SARS-COV-2 in England (Public Health England, 2021) was a nested case-control study estimating the odds ratios for household members becoming secondary cases if the case was vaccinated within 21 days or more before testing positive, vs. household members where the case was not vaccinated (see Chapter 5.2 for more details on this study).

 

14.2.2.2 Sources of exposure and outcome data

 

Data sources for vaccine studies largely rely on vaccine-preventable infectious disease surveillance (for effectiveness studies) and vaccine registries or vaccination data recorded in healthcare databases (for safety and effectiveness studies). Considerations on validation of exposure and outcome data are provided in Chapter 5.3.

 

Infectious disease surveillance is a population-based, routine public health activity involving systematic data collection to monitor epidemiological trends over time in a defined catchment population, and can use various indicators. Data can be obtained from reference laboratories, outbreak reports, hospital records or sentinel systems, and use consistent case definitions and reporting methods. Usually there is no known population denominator thus surveillance data cannot be used to measure incidence. Limitations include under-detection/under-reporting (if passive surveillance), or conversely, over-reporting due to improvements in case detection or introduction of new vaccines with increased disease awareness. Changes/delays in case counting or reporting can artificially reduce the number of reported cases thus artificially increasing vaccine effectiveness. Infectious Disease Surveillance (International Encyclopedia of Public Health 2017;222-229) is a comprehensive review including definitions, methods, and considerations on use of surveillance data in vaccine studies. The chapter on Routine Surveillance of Infectious Diseases in Modern Infectious Disease Epidemiology (J. Giesecke. 3rd Ed. CRC Press 2017) discusses how data for surveillance are collected and interpreted and identifies several sources of potential bias.

 

Access to valid surveillance data for SARS-CoV-2 infection is of particular importance for studies evaluating the effectiveness of COVID-19 vaccines against variants of concern. Such epidemic surveillance data can be obtained, for example, from the ECDC COVID-19 Dashboard.

 

Examples of vaccination registries, and challenges in developing such registries, are discussed in a special journal issue on Vaccine registers--experiences from Europe and elsewhere (Euro Surveill. 2012;17(17):20159), in Validation of the new Swedish vaccination register - Accuracy and completeness of register data (Vaccine 2020; 38(25):4104-10), and in Establishing and maintaining the National Vaccination Register in Finland (Euro Surveill. 2017;22(17):30520).

 

14.2.2.3. Traditional cohort and case-control designs

 

Generic protocols for retrospective case-control studies and retrospective cohort studies to assess the effectiveness of rotavirus and influenza vaccination in EU Member States based on computerised databases were published by the European Centre for Disease Prevention and Control (ECDC). They describe the information that should be collected by country and region in vaccine effectiveness studies and the data sources that may be available to identify virus-related outcomes a vaccine is intended to avert, including hospital registers, computerised primary care databases, specific surveillance systems (i.e. laboratory surveillance, hospital surveillance, primary care surveillance) and laboratory registers. The DRIVE project has developed a similar Core protocol for population-based database cohort-studies. These templates can potentially be used as a guide for the design of effectiveness studies for vaccines other than influenza vaccines.

 

The case-control methodology is frequently used to evaluate vaccine effectiveness post-authorisation but the potential for bias and confounding in such studies are important limitations. The articles Case-control vaccine effectiveness studies: Preparation, design, and enrollment of cases and controls (Vaccine 2017; 35(25):3295-302) and Case-control vaccine effectiveness studies: Data collection, analysis and reporting results (Vaccine 2017; 35(25):3303-8) summarize the recommendations of an expert group regarding best practices for the design, analysis and reporting of case-control vaccine effectiveness studies.

Based on a meta-analysis comprising 49 cohort studies and 10 case-control studies, Efficacy and effectiveness of influenza vaccines in elderly people: a systematic review (Lancet 2005;366(9492):1165-74) highlights the heterogeneity of outcomes and study populations included in such studies and the high likelihood of selection bias.

 

Non-specific effects of vaccines, such as a decrease of mortality, have been claimed in observational studies but generally can be affected by bias and confounding. Epidemiological studies of the 'non-specific effects' of vaccines: I--data collection in observational studies (Trop Med Int Health 2009;14(9):969-76.) and Epidemiological studies of the non-specific effects of vaccines: II--methodological issues in the design and analysis of cohort studies (Trop Med Int Health 2009;14(9):977-85) provide recommendations for vaccine observational studies conducted in countries with high mortality; however, these recommendations have wider relevance. The study Observational studies of non-specific effects of Diphtheria-Tetanus-Pertussis vaccines in low-income countries: Assessing the potential impact of study characteristics, bias and confounding through meta-regression (Vaccine. 2019;37(1):34–40) used meta-regression to analyse study characteristics significantly associated with increased relative risks of non-specific effects of DTP vaccines.

 

The cohort design has been used to monitor the effectiveness of COVID-19 vaccines in mass immunisation settings. BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting (N Engl J Med. 2021;384(15):1412-1423) used data from a nationwide health care organisation to match vaccinated and unvaccinated persons according to demographic and clinical characteristics to assess effectiveness against documented infection, symptomatic infection, COVID-19 related hospitalisation, severe illness, and death. BNT162b2 vaccine effectiveness in preventing asymptomatic infection with SARS-CoV-2 virus: a nationwide historical cohort study (Open Forum Infectious Diseases, 2021; ofab262) used data from of a large health maintenance organisation to compare vaccinated and unvaccinated individuals repeatedly tested for SARS-CoV-2 infection.

 

14.2.2.4. Test negative design

 

The test-negative design aims to reduce bias associated with confounding by health-care-seeking behaviour. The article The test-negative design for estimating influenza vaccine effectiveness (Vaccine 2013;31(17):2165-8) explains the rationale, assumptions and analysis of the test-negative design, originally developed for influenza vaccines. Study subjects were all persons who seek care for an acute respiratory illness and influenza VE was estimated from the ratio of the odds of vaccination among subjects testing positive for influenza to the odds of vaccination among subject testing negative. This design is less susceptible to bias due to misclassification of infection and the confounding by health care-seeking behaviour, at the cost of difficult-to-test assumptions. Test-Negative Designs: Differences and Commonalities with Other Case-Control Studies with "Other Patient" Controls (Epidemiology. 2019 Nov;30(6):838-844) discusses the advantages and disadvantages of the test-negative design in various circumstances.

 

Effectiveness of rotavirus vaccines in preventing cases and hospitalizations due to rotavirus gastroenteritis in Navarre, Spain (Vaccine 2012;30(3):539-43) used a test negative case-control design based on electronic clinical reports. Cases were children with confirmed rotavirus and controls were those who tested negative for rotavirus in all samples. The test-negative design was based on an assumption that the rate of gastroenteritis caused by pathogens other than rotavirus is the same in both vaccinated and unvaccinated persons. This approach may rule out differences in parental attitude when seeking medical care and of physician differences in making decisions about stool sampling or hospitalisation. A limitation is sensitivity of antigen detection which may underestimate vaccine effectiveness. In addition, if virus serotype is not available, it is not possible to study the association between vaccine failure and a possible mismatch of vaccine strains and circulating strains of virus.

 

The article Theoretical basis of the test-negative study design for assessment of influenza vaccine effectiveness (Am J Epidemiol. 2016;184(5):345-53; see also the related Comments) uses directed acyclic graphs to characterize potential biases in studies using this design and shows how bias can be avoided or minimised and where bias may be introduced with particular design variations. The DRIVE project has developed a Core protocol for test-negative design studies which outlines the key elements of the test-negative design, applied to influenza vaccines.

 

The article 2012/13 influenza vaccine effectiveness against hospitalised influenza A(H1N1)pdm09, A(H3N2) and B: estimates from a European network of hospitals (EuroSurveill 2015;20(2):pii=21011) illustrates a multicentre test-negative case-control study to estimate influenza VE in 18 hospitals. It is believed that confounding due to health-seeking behaviour is minimised since, in the study sites, all people needing hospitalisation are likely to be hospitalised. The study Trivalent inactivated seasonal influenza vaccine effectiveness for the prevention of laboratory-confirmed influenza in a Scottish population 2000 to 2009 (EuroSurveill 2015;20(8):pii=21043) applied this method using a Scotland-wide linkage of patient-level primary care, hospital and virological swab data over nine influenza seasons and discusses strengths and weaknesses of the design in this context.

 

Postlicensure Evaluation of COVID-19 Vaccines (JAMA. 2020 Nov 17;324(19):1939-1940) describes methodological challenges of the test-negative case-control design applied to the evaluation of COVID-19 vaccine effectiveness and discusses potential solutions to reduce bias.

 

The study Effectiveness of the Pfizer-BioNTech and Oxford-AstraZeneca vaccines on covid-19 related symptoms, hospital admissions, and mortality in older adults in England: test negative case-control study (BMJ. 2021;373:n1088) linked routine community testing data and vaccination data from the UK National Immunisation Management System to estimate the effect of vaccination on confirmed symptomatic infection, COVID-19 related hospital admissions and case fatality and estimated the odds ratios for testing positive to SARS-CoV-2 in vaccinated compared with unvaccinated people with compatible symptoms tested using polymerase chain reaction (PCR). The study also provides considerations on strengths and limitations of the test-negative design applied to COVID-19 vaccine effectiveness studies.

 

14.2.2.5. Case-population, case-coverage, and screening methods

 

These methods are related and are in some occasions also applied to vaccine safety studies. All include, to some extent, an ecological component such as vaccine coverage or infectious disease surveillance data at population level. Terms to refer to these designs are sometimes used interchangeably. The case-coverage design is mentioned above in paragraph 14.2.1.3. Case-population studies are described in Chapter 5.4.7 and in Vaccine Case-Population: A New Method for Vaccine Safety Surveillance (Drug Saf. 2016;39(12):1197-1209).

 

The screening method estimates vaccine effectiveness by comparing vaccination coverage in positive (usually laboratory confirmed) cases of a disease (e.g. influenza) with the vaccination coverage in the population from which the cases are derived (e.g., the same age group). If representative data on cases and vaccination coverage are available, it can provide an inexpensive and ready-to-use method that can be useful in providing early effectiveness estimates or identify changes in effectiveness over time. However, Application of the screening method to monitor influenza vaccine effectiveness among the elderly in Germany (BMC Infect Dis. 2015;15(1):137) emphasises that accurate and age-specific vaccine coverage rates are crucial to provide valid VE estimates. Since adjusting for important confounders and the assessment of product-specific VE is generally not possible, this method should be considered only a supplementary tool for assessing crude VE.

 

14.2.2.6. Indirect cohort (Broome) method

 

The indirect cohort method is a case-control type design which uses cases caused by non-vaccine serotypes as controls. Use of surveillance data to estimate the effectiveness of the 7-valent conjugate pneumococcal vaccine in children less than 5 years of age over a 9 year period (Vaccine 2012;30(27):4067-72) applied this method to evaluate the effectiveness of a pneumococcal conjugate vaccine against invasive pneumococcal disease (IPD) and compared the results to the effectiveness measured using a standard case-control study conducted during the same time period. The authors considered the method would be most useful shortly after vaccine introduction, and less useful in a setting of very high vaccine coverage and fewer vaccine-type cases.

 

Using the indirect cohort design to estimate the effectiveness of the seven valent pneumococcal conjugate vaccine in England and Wales (PLoS One 2011;6(12):e28435) and Effectiveness of the seven-valent and thirteen-valent pneumococcal conjugate vaccines in England: The indirect cohort design, 2006-2018 (Vaccine. 2019;37(32):4491-4498) describe how the method was used to estimate effectiveness of various vaccine schedules as well as for each vaccine serotype.

 

14.2.2.7. Density case-control design

 

Effectiveness of live-attenuated Japanese encephalitis vaccine (SA14-14-2): a case-control study (Lancet 1996;347(9015):1583-6) describes a case control study of incident cases in which the control group consisted of all village-matched children of a given age who were at risk of developing disease at the time that the case occurred (density sampling). The effect measured is an incidence density rate ratio. Vaccine Effectiveness of Polysaccharide Vaccines Against Clinical Meningitis - Niamey, Niger, June 2015 (PLoS Curr. 2016;8) conducted a case-control study comparing the odds of vaccination among suspected meningitis cases to controls enrolled in a vaccine coverage survey performed at the end of the epidemic. A simulated density case-control design randomly attributing recruitment dates to controls based on case dates of onset was used to compute vaccine effectiveness.

 

14.2.2.8. Impact assessment

 

Vaccine impact studies measure the indirect, total and overall effects of a vaccine, either before/after a vaccination campaign or between two populations during the vaccination campaign, and are largely based on ecological designs; for an overview, see Vaccine effects and impact of vaccination programmes in post-licensure studies (Vaccine. 2013;31(48):5634-42). For example, for a paediatric vaccine, the impact of vaccination can be quantified in the age group targeted for vaccination (overall effect) or in children of other age groups (indirect effect). A generic study protocol to assess the impact of rotavirus vaccination in EU Member States has been published by the ECDC. It recommends the information that needs to be collected to compare the incidence/proportion of rotavirus cases in the period before and after the introduction of the vaccine. These generic protocols need to be adapted to each country/regions and specific situation. Direct and indirect effects in vaccine efficacy and effectiveness (Am J Epidemiol. 1991;133(4):323-31) describes how parameters intended to measure direct effects must be robust and interpretable in the midst of complex indirect effects of vaccine intervention programmes.

 

Impact of rotavirus vaccination in regions with low and moderate vaccine uptake in Germany (Hum Vaccin Immunother 2012; 8(10):1407-15) describes an impact assessment of rotavirus vaccination comparing the incidence rates of hospitalisations before, and in seasons after, vaccine introduction using data from national mandatory disease reporting system. First year experience of rotavirus immunisation programme in Finland (Vaccine 2012; 31(1):176-82) estimates the impact of a rotavirus immunisation programme on the total hospital inpatient and outpatient treated acute gastroenteritis burden and on severe rotavirus disease burden during the first year after introduction. The study may be considered as a vaccine-probe-study, where unspecific disease burden prevented by immunisation is assumed to be caused by the agent the vaccine is targeted against. The study Lack of impact of rotavirus vaccination on childhood seizure hospitalizations in England - An interrupted time series analysis (Vaccine 2018; 36(31):4589-92) discusses possible reasons for negative findings in this study although previous studies have established a protective vaccine association in this age group.

 

In a review of 65 included articles, Population-level impact and herd effects following the introduction of human papillomavirus vaccination programmes: updated systematic review and meta-analysis (Lancet. 2019;394(10197):497–509) compared the frequency (prevalence or incidence) of several HPV-related endpoints between the pre-vaccination and post-vaccination periods with stratification by sex, age, and years since introduction of HPV vaccination.

 

Impact and effectiveness of mRNA BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases, hospitalisations, and deaths following a nationwide vaccination campaign in Israel: an observational study using national surveillance data (Lancet. 2021;397(10287):1819-1829) evaluated the nationwide public-health impact following the widespread introduction of the vaccine using national surveillance and vaccine uptake data. Although such population-level data are ecological, and teasing apart the impact of the vaccination programme from the impact of non-pharmaceutical interventions is complex, declines in incident cases of SARS-CoV-2 by age group were aligned with high vaccine coverage rather than initiation of the nationwide lockdown. Even after re-opening occurred, incidence remained low, suggesting that high vaccine coverage might provide a sustainable path towards resuming normal activity.

 

The effectiveness of currently available COVID-19 vaccines suggests a potential for a population-level effect, which is critical to control the pandemic. Community-level evidence for SARS-CoV-2 vaccine protection of unvaccinated individuals (Nat Med. 2021) used methods to measure this effect by analysing vaccination records and test results in a large population from 177 geographically defined communities, while mitigating the confounding effect of natural immunisation and the spatiotemporally dynamic nature of the epidemic. The results suggest that vaccination not only protects vaccinated individuals but also provides cross-protection to unvaccinated individuals in the community.

 

14.2.2.9. Cluster design

 

A cluster is a group of subjects sharing common characteristics, such as geographical (community, administrative area), health-related (hospital), educational (schools), social (household). In cluster randomised trials, clusters instead of individual subjects are randomly allocated to an intervention, whereas in infectious disease epidemiology studies clusters are sampled based on aspects of transmission (e.g. within a community). This design is often used in low and middle income settings and can measure vaccination interventions naturally applied at the cluster level or when the study objectives require a cluster design (e.g. to estimate herd immunity).

 

The core Protocol_for_Cluster_Investigations_to_Measure_Influenza_Vaccine_Effectiveness builds on the cluster design to generate rapid/early influenza season estimates in settings where vaccination records might be easily obtainable and investigation can take place at the same time as vaccination is carried out (e.g. schools, care homes).

 

In Post-authorisation passive enhanced safety surveillance of seasonal influenza vaccines: protocol of a pilot study in England (BMJ Open. 2017;7(5):e015469) the effect of clustering by GP practice was examined. Meningococcal B Vaccine and Meningococcal Carriage in Adolescents in Australia (N Engl J Med. 2020 Jan 23;382(4):318-327) used cluster randomisation to assign students, according to school, to receive 4CMenB vaccination either at baseline or at 12 months (control) to measure oropharyngeal carriage.

 

In The ring vaccination trial: a novel cluster randomised controlled trial design to evaluate vaccine efficacy and effectiveness during outbreaks, with special reference to Ebola (BMJ. 2015 Jul 27;351:h3740), a newly diagnosed Ebola case served as the index case to form a “ring”, which was then randomised to immediate or delayed vaccination with inclusion based on tracing cases using active surveillance instead of randomisation.

 

The Prospective study to evaluate the safety, effectiveness and impact of the RTS,S/AS01E malaria vaccine in young children in sub-Saharan Africa is using active surveillance to enrol large numbers of children in vaccinated and unvaccinated clusters as part of the WHO Malaria Vaccine Implementation Programme to conduct temporal (before/after) and concurrent (exposed vs. unexposed clusters) comparisons. Clusters are selected based on geographically limited areas with demographic surveillance in place and infrastructure to monitor population health and vaccination programmes.

 

14.2.2.10. Methods to study waning immunity

 

The study of vaccine effectiveness against diseases where immunity wanes over time requires consideration of both the within-host dynamics of the pathogen and immune system as well as the associated population-level transmission dynamics. Implications of vaccination and waning immunity (Proc Biol Sci 2009; 276(1664):2071-80) seeks to combine immunological and epidemiological models for measles infection to examine the interplay between disease incidence, waning immunity and boosting.

 

Besides a discussion on effectiveness of varicella vaccines over time, Global Varicella Vaccine Effectiveness: A Meta-analysis (Pediatrics 2016; 137(3):e20153741) reports low effectiveness in outbreak investigations and highlights the difficulties to reliably measure effectiveness in this situation where some confounders cannot be controlled for, the force of infection may be high, the degree of exposure may be variable across study participants and measures may originate from settings where there is epidemiologic evidence of vaccine failure. More than a few estimates are therefore needed to accurately assess vaccine effectiveness and conclude in waning immunity. 

 

14.2.2.11. Misclassification in studies of vaccine effectiveness

 

Like vaccine safety studies, studies of vaccine effectiveness rely on accurate identification of vaccination and cases of vaccine-preventable diseases but in practice, diagnostic tests, clinical case definitions and vaccination records often present inaccuracies. For outcomes with a complex natural history, and particularly when using secondary data collection (where case finding may be difficult), such as neurological or potential immune mediated diseases, validation studies based on case validation may be needed in a first step. Bias due to differential and non-differential disease- and exposure misclassification in studies of vaccine effectiveness (PLoS One 2018;15;13(6):e0199180) explores through simulations the impact of non-differential and differential disease- and exposure-misclassification when estimating vaccine effectiveness using cohort, case-control, test-negative case-control and case-cohort designs.

 

Misclassification can lead to significant bias and its impact strongly depends on the vaccination scenarios. A web application designed in the ADVANCE project is publicly available to assess the potential (joint) impact of possibly differential disease- and exposure misclassification.

 

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