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ENCePP Guide on Methodological Standards in Pharmacoepidemiology


15.2. Vaccine safety and effectiveness


15.2.1. Vaccine safety General considerations


The ADVANCE Report on appraisal of vaccine safety methods (2014) describes a wide range of direct and indirect methods of vaccine risk assessment. It emphasises how vaccines differ from other medicines in this context, evaluates study designs, and provides recommendations. Vaccination Programmes | Epidemiology, Monitoring, Evaluation (Hahné, S., Bollaerts, K., & Farrington, P., Routledge, 2021) is a comprehensive textbook addressing most of the concepts presented in this chapter. Specific aspects related to vaccine safety are also discussed in several documents and guidances:

  • 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 Guide to active vaccine safety surveillance: Report of CIOMS working group on vaccine safety – executive summary (Vaccine 2017;35(32):3917-21) describes the process for determining the need for active vaccine safety surveillance, more specifically in the context of resource-limited countries, and, if so, for choosing the best type of active safety surveillance, 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.

  • 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.

  • 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 (EMA, 2013) 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 (WHO, 2020) has been developed upon recommendation and guidance of the WHO Global Advisory Committee on Vaccine Safety (GACVS) and other experts, and describes four categories of surveillance strategies: passive surveillance, active surveillance, cohort event monitoring, and sentinel surveillance. While developed for COVID-19 vaccines, this manual can be used to guide pandemic preparedness activities for the monitoring of novel vaccines.

The article Current Approaches to Vaccine Safety Using Observational Data: A Rationale for the EUMAEUS (Evaluating Use of Methods for Adverse Events Under Surveillance-for Vaccines) Study Design (Front Pharmacol. 2022;13:837632) provides an overview of the strengths and limitations of study designs used to monitor vaccine safety and discusses the assumptions made to mitigate bias in such studies.


There is increasing interest in the influence of genetics/genomics on safety and efficacy outcomes of vaccination. Research in this field is illustrated in 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; 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 vaccine safety and effectiveness. In State-wide genomic epidemiology investigations of COVID-19 in healthcare workers in 2020 Victoria, Australia: Qualitative thematic analysis to provide insights for future pandemic preparedness (Lancet Reg Health West Pac. 2022;25:100487), a large SARS-CoV-2 genomic epidemiological investigation identified transmission dynamics in healthcare workers using a newly developed set of metadata, illustrating the increasing role of genomics in pharmacoepidemiology (see Chapter 15.3). Genetic risk and incident venous thromboembolism in middle-aged and older adults following Covid-19 vaccination (2022) used data from the UK Biobank to estimate hazard ratios of the associations between a polygenic risk score and post-vaccination incident veinous thromboembolism. Signal detection and validation


Besides a qualitative analysis of spontaneous case reports or case series, quantitative methods such as disproportionality analyses (described in Chapter 10) and observed-to-expected (O/E) analyses are routinely employed in signal detection and validation 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.


Signaling COVID-19 Vaccine Adverse Events (Drug Saf. 2022 Jun 23:1–16) discusses the extent, direction, impact, and causes of masking, an issue associated with signal detection methodologies in which signals for a product of interest are hidden by the presence of other reported products, which may limit the understanding of the risks associated with COVID-19 vaccines, as well as other vaccines, and delay their identification.


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 (EMA, 2013) suggests conducting 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: an 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 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) illustrates 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. 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;283:154-65)).


Several studies have generated background incidence rates of Adverse Events of Special Interest (AESIs) for COVID-19 vaccines and discuss methodological challenges related to identifying these events in electronic health records (EHRs). 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 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 (2021) has been developed by the vACcine Covid-19 monitoring readinESS (ACCESS) consortium (data available on the VAC4EU platform). Other published templates include the FDA Best Initiative’s protocol for Background Rates of Adverse Events of Special Interest for COVID-19 Vaccine Safety Monitoring (FDA, 2021), and the Template for observational study protocols for sentinel surveillance of adverse events of special interest (AESIs) after vaccination with COVID-19 vaccines (WHO, 2021) which describes study designs for hospital case-based monitoring of pre-defined AESIs following COVID-19 vaccination in all age groups.


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;373:n1435) and Background rates of five thrombosis with thrombocytopenia syndromes of special interest for COVID-19 vaccine safety surveillance: Incidence between 2017 and 2019 and patient profiles from 38.6 million people in six European countries (Pharmacoepidemiol Drug Saf. 2022;31(5):495-510).


Historical comparator designs, which compare background rates of events in a general population vs. observed rates amongst a vaccinated cohort are commonly used but may generate false positives, as discussed in Bias, Precision and Timeliness of Historical (Background) Rate Comparison Methods for Vaccine Safety Monitoring: An Empirical Multi-Database Analysis (Front Pharmacol. 2021;12:773875): the authors evaluate strategies for estimating background rates and the effect of empirical calibration on type 1 and 2 errors using outcomes presumed to be unrelated to vaccines (negative control outcomes) as well as imputed positive controls (outcomes simulated to be caused by the vaccines). Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases (Front Pharmacol. 2022:814198) uses 12 data sources to systematically examine the impact of the choice of analysis parameters such as target population, anchoring event, time-at-risk, and data source, on the estimation of background incidence rates, and shows that rates are highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start, the choice of the database, clean window choice and time-at-risk duration, and less so by secular or seasonal trends.


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) (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 VSD 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 pharmacoepidemiological study. 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 (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. It builds on this method but does not require pre-specified outcomes or specific post-exposure risk periods. 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. Study designs for vaccine safety assessment


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 (2014) and in Part IV of the book Vaccination Programmes | Epidemiology, Monitoring, Evaluation (Hahné, S., Bollaerts, K., & Farrington, P., Routledge, 2021).


Case-only designs


Traditional study designs such as cohort and case-control studies (see Chapter 4.2) may be difficult to implement for vaccines in circumstances of high vaccine coverage (for example, in mass immunisation campaigns such as for COVID-19), a lack of an appropriate unvaccinated group, or a lack of adequate information on covariates at the individual level. Frequent sources of confounding are socioeconomic status, underlying health status, and factors influencing the probability of being vaccinated such as access to healthcare or belonging to a risk group. In such situations, case-only designs may provide stronger evidence than large cohort studies as they control for fixed individual-level confounders (such as demographics, genetics and social deprivation) and have similar, sometimes higher, power (see Control without separate controls: evaluation of vaccine safety using case-only methods, Vaccine 2004;22(15-16):2064-70). Case-only designs are presented in Chapter 4.2.3 and a detailed discussion of the self-controlled case series (SCCS), and the self-controlled risk interval (SCRI) methods is provided in Chapter 4.4.3.


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

Using alternative approaches, e.g., a cohort design and sensitivity analyses using a self-controlled method, provides an opportunity for minimising some biases that cannot be taken into account in the primary design. This is increasingly considered good practice, as reflected by many of the recent studies on the safety of COVID-19 vaccines.


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 SCRI method can be used to shorten observation time (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 (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 (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 is an adequate method to study vaccine safety, provided the main requirements of the method are taken into account (see Chapter 4.4.3). 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). In First dose ChAdOx1 and BNT162b2 COVID-19 vaccinations and cerebral venous sinus thrombosis: A pooled self-controlled case series study of 11.6 million individuals in England, Scotland, and Wales (PLoS Med. 2022;19(2):e1003927), pooled primary care, secondary care, mortality, and virological data from England, Scotland, and Wales were used to perform a SCCS analysis of incident cerebral venous sinus thrombosis (CVST). The authors discuss the possibility that the SCCS assumption of event-independent exposure may not have been satisfied in the case of CVST, since vaccination prioritised the clinically vulnerable and those with underlying conditions, which may have caused a selection effect where individuals more likely to have an event were less likely to be vaccinated and thus less likely to be included in the analyses. In First-dose ChAdOx1 and BNT162b2 COVID-19 vaccines and thrombocytopenic, thromboembolic and hemorrhagic events in Scotland (Nat Med. 2021; 27(7):1290-7), potential residual confounding by indication in the primary analysis (nested case-control design) was addressed by a SCCS to adjust for time-invariant confounders. Risk of acute myocardial infarction and ischaemic stroke following COVID-19 in Sweden: a self-controlled case series and matched cohort study (Lancet 2021;398(10300):599-607) showed that a COVID-19 diagnosis is an independent risk factor for first acute myocardial infarction and ischaemic stroke, using two complementary designs in Swedish healthcare data: a SCCS to calculate incidence rate ratios in temporal risk periods following COVID-19 onset, and a matched cohort study to compare the risk of these events within 2 weeks following COVID-19 to the risk in the background population.


A modified self-controlled case series method for event-dependent exposures and high event-related mortality, with application to COVID-19 vaccine safety (Stat Med. 2022;41(10):1735-50) uses both real data from a study of the risk of cardiovascular events, and simulated data, to describe how to handle both event-dependent exposures and high event-related mortality and proposes a newly developed test to determine whether the vaccine has the same effect (or lack of effect) at different doses.


Cohort-event monitoring


Prospective cohort-event monitoring including active surveillance of vaccinated subjects using smartphone applications and/or 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 were 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 ( (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. 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;21(7):939-49) 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. COVID-19 vaccine waning and effectiveness and side-effects of boosters: a prospective community study from the ZOE COVID Study (Lancet Infect Dis. 2022:S1473-3099(22)00146-3) used SARS-CoV-2 positivity rates in individuals from a longitudinal, prospective, community-based study, in which data were self-reported through an app, to assess 16 self-reported systemic and localised adverse reactions of COVID-19 booster doses, in addition to effectiveness against infection. 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;39(29):3844-51) prospectively monitored residents of nursing homes using electronic health record data on vaccinations and pre-specified adverse events and compared to unvaccinated residents during the same time period. The study Cohort Event Monitoring of safety of COVID-19 vaccines (Early-) Covid-Vaccine-Monitor (2022) generates incidence rates of vaccine-related adverse reactions for different COVID-19 vaccines in the general population and special populations (pregnant and lactating women, children and adolescents, immunocompromised, people with history of allergy, and people with prior SARS-CoV-2 infection). Data are prospectively collected directly from vaccine recipients using four different data capture systems in several EU countries.


Case-coverage design


The case-coverage design is a type of ecological design using 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 (and vaccination) and awareness of possible occurrence of a specific outcome, and does not consider underlying medical conditions, with 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


Generic protocols, also referred to as template protocols or master protocols, have been developed by some organisations to support swift implementation of studies monitoring the safety of COVID-19 vaccines, mostly based, in Europe, on the EMA Guidance for the format and content of the protocol of non-interventional post-authorisation safety studies (2012). The ACCESS consortium has published four Template study protocols (2021) 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 (2021) and a Master Protocol: Assessment of Risk of Safety Outcomes Following COVID-19 Vaccination (2021). Although developed for COVID-19 vaccines, these protocols can be tailored to other exposures and outcomes, as they address the most important points to consider in vaccine safety studies. Meta-analyses


The guidance on conducting meta-analyses of pharmacoepidemiological studies of safety outcomes (Annex 1 of this Guide) can also be applied 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 quality and limitations of 121 meta-analyses. 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. The Systematic review and meta-analysis of the effectiveness and perinatal outcomes of COVID-19 vaccination in pregnancy (Nat Commun. 2022;13(1):2414) generated evidence on a large number of adverse pregnancy and perinatal outcomes. With the increasing use of multi-database studies to assess rare vaccine safety outcomes, meta-analytical methods are often used to combine data generated at country level to obtain pooled risk estimates in large populations: in SARS-CoV-2 Vaccination and Myocarditis in a Nordic Cohort Study of 23 Million Residents (JAMA Cardiol. 2022;7(6):600-12), four cohort studies were conducted in linked nationwide health registers in Denmark, Finland, Norway, and Sweden according to a common protocol; the results were combined using meta-analysis and the homogeneity of country-specific estimates was tested. 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 when evaluating vaccine safety in these populations, especially infants and children who often differ from healthy individuals and change rapidly during the first few years of life, and elderly individuals.


Pregnant and breastfeeding persons


This population represents 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. Annex 2 of this Guide provides guidance on methods to evaluate medicines in pregnancy and breastfeeding and discusses important aspects of study design that should also be considered for vaccine studies. The Guidance for design and analysis of observational studies of fetal and newborn outcomes following COVID-19 vaccination during pregnancy (Vaccine 2021;39(14):1882-6) 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;39(29):3814-24) 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. COVID-19 Vaccines: safety surveillance manual. Module on safety surveillance of COVID-19 vaccines in pregnant and breastfeeding women (WHO, 2021) provides guidance for the active surveillance of maternal and neonatal events, including on case definitions and methods. In the population-based retrospective cohort study Association of SARS-CoV-2 Vaccination During Pregnancy With Pregnancy Outcomes (JAMA. 2022;327(15):1469-77), the Swedish Pregnancy Register and the Norwegian Medical Birth Registry were linked to vaccination and other registers and compared vaccinated and unvaccinated subjects, showing that vaccination in pregnancy was not associated with risks of preterm birth, stillbirth, small for gestational age and other outcomes. Spontaneous Abortion Following COVID-19 Vaccination During Pregnancy (JAMA. 2021;326(16):1629-31) applied a validated pregnancy algorithm, which incorporates diagnostic and procedure codes and electronic health record data, to identify and assign gestational ages for spontaneous abortions and ongoing pregnancies in the US Vaccine Safety Datalink, and analysed the odds of receiving a COVID-19 vaccine in the 28 days prior to spontaneous abortion compared with the 28 days prior to index dates for ongoing pregnancies.


Pregnancy registries can be used to assess pregnancy and neonatal outcomes (see Chapter 7.3.6). 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) describes methods to calculate rates of miscarriage, addresses the lack of knowledge of time of conception during which vaccination might confer risk, and performs 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 immunisation. The Systematic overview of data sources for Drug Safety in pregnancy research (2016) provides an inventory of pregnancy exposure registries and alternative data sources useful to assess the safety of prenatal vaccine exposure.


Immunocompromised persons


Post-authorisation studies are often required for this population as immunocompromised subjects are 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 immunocompromised status and possible limitations due to residual confounding, differences within and between etiological groups and small sample size in some of these 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.


15.2.2. Vaccine effectiveness General considerations


The book Vaccination Programmes | Epidemiology, Monitoring, Evaluation (Hahné, S., Bollaerts, K., & Farrington, P., Routledge, 2021) discusses the concept of vaccine effectiveness and provides further insight into the methods (and issues) discussed in this section. The book 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.


A key reference is Vaccine effects and impact of vaccination programmes in post-licensure studies (Vaccine 2013;31(48):5634-42), which reviews methods for the evaluation of the effectiveness of vaccines and vaccination programmes and discusses design assumptions and potential biases to consider. 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.


Evaluation of influenza vaccine effectiveness: a guide to the design and interpretation of observational studies (WHO, 2017) provides an overview of methods to study influenza vaccine effectiveness, also relevant for other vaccines. Evaluation of COVID-19 vaccine effectiveness (WHO, 2021) provides interim best practice guidance on how to monitor COVID-19 vaccine effectiveness using observational study designs, including considerations relevant to low- and middle-income countries.


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


The template protocols (2021) 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. The Core protocol for ECDC studies of COVID-19 vaccine effectiveness against hospitalisation with Severe Acute Respiratory Infection laboratory-confirmed with SARS-CoV-2 (ECDC, 2021) presents the main elements to consider to design multi-centre, multi-country hospital-based COVID-19 vaccine effectiveness studies in patients hospitalised with severe acute respiratory infections (SARI).


Although focusing on the planning, evaluation, and modelling of vaccine efficacy trials, Challenges of evaluating and modelling vaccination in emerging infectious diseases (Epidemics 2021:100506) includes a useful summary of references for the estimation of indirect, total, and overall effects of vaccines. 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 both safety and effectiveness studies). Considerations on validation of exposure and outcome data are provided in Chapter 4.3.


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;222-9) 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 surveillance data are collected and interpreted, and identifies sources of potential bias. Chapter 8 of Vaccination Programmes | Epidemiology, Monitoring, Evaluation outlines the main methods of vaccine-preventable disease surveillance, considering data sources, case definitions, biases and methods for descriptive analyses.


Access to valid SARS-CoV-2 epidemiological surveillance data has been of particular importance for studies of the effectiveness of COVID-19 vaccines against variants of concern. Previously made available by the European Centre for Disease Prevention and Control (ECDC), such date is available from the WHO Coronavirus (COVID-19) Dashboard which also includes vaccine coverage data.   


Examples of vaccination registries, and challenges in developing such registries, are discussed in Vaccine registers-experiences from Europe and elsewhere (Euro Surveill. 2012;17(17):20159), Validation of the new Swedish vaccination register - Accuracy and completeness of register data (Vaccine 2020; 38(25):4104-10), and Establishing and maintaining the National Vaccination Register in Finland (Euro Surveill. 2017;22(17):30520). Developed by WHO, Public health surveillance for COVID-19: interim guidance describes key aspects of the implementation of SARS-CoV-2 surveillance, including a section on vaccine effectiveness monitoring in relation to surveillance systems. Study designs for vaccine effectiveness assessment


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 are published by ECDC. They describe the information that should be collected at national and regional level and potential data sources to identify virological outcomes, including hospital registers, primary care databases, surveillance systems (laboratory, hospital, primary care) and laboratory registers. The DRIVE project has developed a similar Core protocol for population-based database cohort-studies. These templates can be used to guide the design of effectiveness studies for vaccines other than influenza vaccines.


The case-control design has been used to evaluate vaccine effectiveness but the likelihood of bias and confounding in such studies is a potential important limitation. 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) provide recommendations on best practices for the design, analysis and reporting of vaccine effectiveness case-control studies. Based on a meta-analysis of 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. In A Dynamic Model for Evaluation of the Bias of Influenza Vaccine Effectiveness Estimates From Observational Studies (Am J Epidemiol. 2019;188(2):451-60), a dynamic probability model was developed to evaluate biases of VE estimates from passive surveillance cohort, test-negative, and traditional case-control studies.


Non-specific effects of vaccines, such as a decrease of mortality, have been claimed in observational studies but 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 observational studies conducted in high mortality settings; 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 design characteristics significantly associated with increased relative risks of non-specific effects of DTP vaccines. 


The cohort design has been widely used to monitor the effectiveness of COVID-19 vaccines; the following two examples reflect early times of the pandemic, and its later phase when several vaccines were used, reaching wider population groups and used according to different types of vaccination schedule depending on national policies: BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting (N Engl J Med. 2021;384(15):1412-23) used data from a nationwide healthcare organisation to match vaccinated and unvaccinated subjects according to demographic and clinical characteristics, to assess effectiveness against infection, COVID-19 related hospitalisation, severe illness, and death. Vaccine effectiveness against SARS-CoV-2 infection, hospitalization, and death when combining a first dose ChAdOx1 vaccine with a subsequent mRNA vaccine in Denmark: A nationwide population-based cohort study (PLoS Med. 2021;18(12):e1003874) used nationwide linked registries to estimate VE against several outcomes of interest of a heterologous vaccination schedule, compared to unvaccinated individuals. As vaccination coverage increased, using a non-vaccinated comparator group became no longer feasible or suitable, and alternative comparators were needed (see paragraph below on comparative effectiveness).


Test-negative case-control design


The test-negative case-control design aims to reduce bias associated with misclassification of infection and confounding by healthcare-seeking behaviour, at the cost of sometimes difficult-to-test assumptions. The test-negative design for estimating influenza vaccine effectiveness (Vaccine 2013;31(17):2165-8) explains the rationale, assumptions and analysis of this design, originally developed for influenza vaccines. Study subjects were all persons seeking 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. Test-Negative Designs: Differences and Commonalities with Other Case-Control Studies with "Other Patient" Controls (Epidemiology. 2019 Nov;30(6):838-44) discusses advantages and disadvantages of the design in various circumstances. The use of test-negative controls to monitor vaccine effectiveness: a systematic review of methodology (Epidemiology 2020;31(1):43-64) reviews 348 articles and discusses challenges of this design for various vaccines and pathogens, also providing a list of recommendations.


In Effectiveness of rotavirus vaccines in preventing cases and hospitalizations due to rotavirus gastroenteritis in Navarre, Spain (Vaccine 2012;30(3):539-43), electronic clinical reports were used to select cases (children with confirmed rotavirus infection) and test-negative controls (children who tested negative for rotavirus in all samples), under the assumption that the rate of gastroenteritis caused by pathogens other than rotavirus is the same in both vaccinated and unvaccinated subjects. 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 between vaccine strains and circulating strains.


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 characterise potential biases and shows how they can be avoided or minimised. In Estimands and Estimation of COVID-19 Vaccine Effectiveness Under the Test-Negative Design: Connections to Causal Inference (Epidemiology 2022;33(3):325-33), an unbiased estimator for vaccine effectiveness using the test-negative design is proposed under the scenario of different vaccine effectiveness estimates across patient subgroups.


In the multicentre study in 18 hospitals 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), influenza VE was estimated based on the assumtion that confounding due to health-seeking behaviour is minimised since all individuals needing hospitalisation are likely to be hospitalised. 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) usd Scotland-wide linkage of patient-level primary care, hospital and virological data over nine influenza seasons, and discusses strengths and weaknesses of the test-negative design in this context.


Postlicensure Evaluation of COVID-19 Vaccines (JAMA. 2020;324(19):1939-40) describes methodological challenges of the test-negative design applied to COVID-19 vaccines and discusses solutions to minimise bias. Covid-19 Vaccine Effectiveness and the Test-Negative Design (N Engl J Med. 2021;385(15):1431-33) uses the example of a published study in a large hospital network to provide considerations on how to report findings and assess their sensitivity to biases specific to the test-negative design. 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 and vaccination data to estimate effectiveness against 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 to unvaccinated subjects with compatible symptoms. The study also provides considerations on strengths and limitations of the test-negative design.


The DRIVE project has developed a Core protocol for test-negative design studies which outlines the key elements of the design applied to influenza vaccines, while the COVIDRIVE consortium has developed a COVIDRIVE TND-VE Master Protocol to assess brand-specific COVID-19 vaccine effectiveness.


Case-population, case-coverage, and screening methods


These methods are related, and are on some occasions also applied to vaccine safety studies. All include, to some extent, an ecological component such as vaccine coverage or epidemiological surveillance data at population level. Terms to refer to these designs are sometimes used interchangeably. The case-coverage design is mentioned above in paragraph Case-population studies are described in Chapter 4.4.7 and in Vaccine Case-Population: A New Method for Vaccine Safety Surveillance (Drug Saf. 2016;39(12):1197-209).


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., in the same age group). If representative data on cases and vaccination coverage are available, it can provide an inexpensive and rapid method, useful for providing early 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 estimates. Since adjusting for important confounders and assessing product-specific VE is generally challenging, this method should be considered mainly as a supplementary tool to assess crude VE.


Indirect cohort (Broome) method


The indirect cohort method is a case-control type design which uses cases caused by non-vaccine serotypes as controls and uses surveillance data, instead of vaccination coverage data, to estimate vaccine effectiveness. 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) evaluated the effectiveness of a pneumococcal conjugate vaccine against invasive pneumococcal disease (IPD) and compared to the results of a standard case-control study conducted during the same time period. The authors consider the method most useful shortly after vaccine introduction, and less useful in a setting of very high vaccine coverage and fewer 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):4491-98) describe how the method was used to estimate effectiveness of various vaccine schedules as well as for each vaccine serotype.


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. In Vaccine Effectiveness of Polysaccharide Vaccines Against Clinical Meningitis - Niamey, Niger, June 2015 (PLoS Curr. 2016;8), a case-control study compared 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. In Surveillance of COVID-19 vaccine effectiveness: a real-time case–control study in southern Sweden (Epidemiol Infect. 2022;150:1-15) a continuous density case-control sampling was performed, with the control group randomly selected from the complete study cohort as individuals without a positive test the same week as the case or 12 weeks prior.


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) highlights the difficulties to reliably measure effectiveness in a situation where some confounders cannot be controlled for, force of infection may be high, degree of exposure may be variable across study participants, and measures may originate from settings where there is evidence of vaccine failure. More than a few estimates are therefore needed to accurately assess vaccine effectiveness and conclude in waning immunity.


Duration of effectiveness of vaccines against SARS-CoV-2 infection and COVID-19 disease: results of a systematic review and meta-regression (Lancet 2022;399(10328):924-944) reviews evidence of changes in efficacy or effectiveness with time since full vaccination for various clinical outcomes. Potential biases in evaluating changes in effectiveness over time, and how to minimise them, are presented in a tabular format.


Vaccine effectiveness estimates over time are subject to bias from differential depletion of susceptibles (persons at risk of infection) between vaccinated and unvaccinated groups, which can lead to biased estimates of waning effectiveness. Depletion-of-susceptibles bias in influenza vaccine waning studies: how to ensure robust results (Epidemiol Infect. 2019;147:e306) recommends to study only vaccinated persons, and compare for each day the incidence in persons with earlier or later dates of vaccination, to assess waning as a function of vaccination time. Identifying and Alleviating Bias Due to Differential Depletion of Susceptible People in Postmarketing Evaluations of COVID-19 Vaccines (Am J Epidemiol. 2022;191(5):800-11) outlines scenarios under which bias can arise and identifies approaches to minimise these biases. Specific aspects of vaccine effectiveness designs


Comparative effectiveness


There are few comparative effectiveness studies of vaccines, except for some head-to-head immunogenicity studies, but comparative effectiveness methods have been used to compare vaccination schedules or vaccine formulations (Analysis of relative effectiveness of high-dose versus standard-dose influenza vaccines using an instrumental variable method, Vaccine 2019;37(11):1484-90; 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). The COVID-19 pandemic and the authorisation of vaccines based on different development platforms has increased the interest in, and triggered, comparative studies. 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. 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. Comparative effectiveness of the BNT162b2 and ChAdOx1 vaccines against Covid-19 in people over 50 (Nat Commun. 2022;13(1):1519) used data from the existing large UK Biobank prospective cohort linked to data from primary care, hospital admissions, and COVID-19 testing data, to compare the effectiveness of BNT162b2 vs. ChAdOx1s against COVID-19 infection and hospitalisation, using propensity score modelling. Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines in U.S. Veterans (N Engl J Med. 2022;386(2):105-15) used a target trial emulation design where recipients of each vaccine were matched in a 1:1 ratio according to their baseline risk factors.


Impact studies


Vaccine impact studies estimate disease reduction in a community. These studies are typically ecological or modelling analyses that compare disease outcomes from pre- and post-vaccine introduction periods. Reductions in disease outcomes are realised through direct effects of vaccination in vaccinated people and indirect effects due to reduced transmission within a community. Sometimes other concurrent interventions or phenomena unrelated to vaccine effects, such as changes in risk behaviours or healthcare practices, can reduce disease outcomes and confound the assessment of vaccine impact (see The value of vaccine programme impact monitoring during the COVID-19 pandemic, Lancet 2022;399(10320):119-21). 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 in other age groups (indirect effect). For an overview, see Vaccine effects and impact of vaccination programmes in post-licensure studies (Vaccine 2013;31(48):5634-42).


A generic study protocol to assess the impact of rotavirus vaccination in EU Member States (2013) has been published by the ECDC. It lists the information that needs to be collected to compare the incidence/proportion of rotavirus cases in the period before and after vaccine introduction. 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.


First year experience of rotavirus immunisation programme in Finland (Vaccine 2012; 31(1):176-82) estimated 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, based on the assumption that unspecific disease burden prevented by immunisation is caused by the agent targeted by the vaccine. 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 although previous studies have established a protective effect in this age group. In a review of 65 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 prevalence or incidence of several HPV-related endpoints between the pre- 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):1819-29) evaluated the nationwide public health impact of 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 by age group were shown to be aligned with high vaccine coverage rather than initiation of the nationwide lockdown.


Accumulated data on the effectiveness of 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;27(8):1367-9) measured this effect by analysing vaccination records and test results in a large population from 177 communities, while mitigating the confounding effect of natural immunity 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.


Transmission studies


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, households can provide early evidence of such impact. Among the first studies of the impact of COVID-19 vaccination on transmission, Effect of Vaccination on Household Transmission of SARS-CoV-2 in England (N Engl J Med. 2021;385(8):759-60) was a nested case-control study estimating 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 4.2 for more details on this study). Vaccination with BNT162b2 reduces transmission of SARS-CoV-2 to household contacts in Israel (Science. 2022;375(6585):1151-54) assessed the effectiveness of BNT162b2 against susceptibility to infection and infectiousness, comparing pre- and post-Delta periods, using a chain binomial model applied to data from a large healthcare organisation. Community transmission and viral load kinetics of the SARS-CoV-2 delta (B.1.617.2) variant in vaccinated and unvaccinated individuals in the UK: a prospective, longitudinal, cohort study (Lancet Infect Dis. 2022;22(2):183-95) ascertained secondary transmission by longitudinally following index cases and their contacts (regardless of symptoms) in the community early after exposure to the Delta variant, and highlights the importance of community studies to characterise transmission in highly vaccinated populations. Specific limitations of transmission studies such as likelihood of information bias (misclassification) and selection bias, should be considered when interpreting findings and are discussed in the above references.


Cluster design


A cluster is a group of subjects sharing common characteristics: geographical (community, administrative area), health-related (hospital), educational (schools), or 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) or a vaccination programme. 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 (ECDC, 2009) 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. in 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;382(4):318-27) 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;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. Assessing the safety, impact and effectiveness of RTS,S/AS01 E malaria vaccine following its introduction in three sub-Saharan African countries: methodological approaches and study set-up (Malar J. 2022;21(1):132) uses 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.


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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