Specific aspects of vaccines to be considered in pharmacovigilance and pharmacoepidemiology have been highlighted in several documents. The Report of the CIOMS/WHO Working Group on Definition and Application of Terms for Vaccine Pharmacovigilance (2012) emphasises that characteristics of the vaccine and the vaccinated population, settings and circumstances of vaccine administration and data analysis issues are worthy of special attention during vaccine safety monitoring. It also provides definitions and explanatory notes for the terms ‘vaccine pharmacovigilance’, ‘vaccination failure’ and ‘adverse event following immunisation (AEFI)’. 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 the Module P.I: Vaccines for prophylaxis against infectious diseases of the Good pharmacovigilance practices (GVP).
Methods for vaccine pharmacovigilance have been developed by the Brighton Collaboration, which provides resources to facilitate and harmonise collection, analysis and presentation of vaccine safety data, including case definitions, an electronic tool to help the classification of reported signs and symptoms, template protocols and guidelines. 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. Module 4 (Surveillance) of the e-learning training course Vaccine Safety Basics of the World Health Organization (WHO) describes phamacovigilance principles, causality assessment procedures, surveillance systems and factors influencing the risk-benefit balance of vaccines. In particular, in contrast to the use of other medicines, vaccines are often used in healthy people and it is not only important to identify possible risks but also to emphasize safety if it does exist. For example a systematic review on influenza vaccination in pregnancy and the risk of congenital anomalies in newborns did not find an association, adding to the evidence base of influenza vaccination in pregnancy (Maternal Influenza Vaccination and Risk for Congenital Malformations: A Systematic Review and Meta-analysis. Obstet Gynecol 2015;126(5):1075-84.).
The 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)335-346) further examines the effects of subgroup analyses based on the relative distribution of vaccine/non-vaccine reports in paediatric ADR data.
The article Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization (Pharmacoepidemiology and drug safety 2013; 22: 477–487) explores various ways of improving performance of signal detection algorithms when looking for vaccines.
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.
When prompt decision-making about a safety concern is required and there is insufficient time to review individual cases, the GVP Module P.I: Vaccines for prophylaxis against infectious diseases suggests the conduct of observed vs. expected (O/E) analyses for signal validation and preliminary signal evaluation. The module discusses key requirements of O/E analyses: the observed number of cases detected in a passive or active surveillance systems, near real-time exposure data, appropriately stratified background incidence rates (to calculate the expected number of cases) and sensitivity analyses around these measures.
Human papilloma virus immunization in adolescents and young adults: a cohort study to illustrate what events might be mistaken for adverse reactions (Pediatr Infect Dis J 2007;26(11):979-84) and Health problems most commonly diagnosed among young female patients during visits to general practitioners and gynecologists in France before the initiation of the human papillomavirus vaccination program (Pharmacoepidemiol Drug Saf 2012; 21(3):261-80) 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 also vary by age, sex, method of ascertainment and geography, as shown in Importance of background rates of disease in assessment of vaccine safety during mass immunisation with pandemic H1N1 influenza vaccines (Lancet 2009; 374(9707):2115-22). Moreover, 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.
Simple ‘snapshot’ O/E analyses are easy to perform but may not be appropriate for continuous monitoring due to inflation of type 1 error rates when multiple tests are performed. Safety monitoring of Influenza A/H1N1 pandemic vaccines in EudraVigilance (Vaccine 2011;29(26):4378-87) illustrates that simple ‘snapshot’ O/E analyses are also affected by uncertainties regarding the numbers of vaccinated individuals and age-specific background incidence rates.
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):1275-1284), 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 Vaccine Safety Datalink concluded that care with data quality, outcome definitions, comparison groups and length of surveillance is required to enable detection of true safety problems 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, more robust but also more complex to perform, understand and communicate to a non-statistical 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 to document the risk of intussusception after rotavirus vaccines (see Intussusception after Rotavirus Vaccination — Spontaneous Reports; N Engl J Med 2011; 365:2139).
Traditional study designs such as cohort and case-control studies may be difficult to implement for vaccines where studies involve populations with high vaccine coverage rates, 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. Control without separate controls: evaluation of vaccine safety using case-only methods (Vaccine 2004; 22(15-16):2064-70) describes and illustrates epidemiological methods that are useful in such situations. They are mostly case-only design described in section 5.3.2 of the Guide.:
The study Control without separate controls: evaluation of vaccine safety using case-only methods (Vaccine 2004; 22(15-16):2064-70) 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 completely for fixed individual-level confounders (such as demographics, genetics and social deprivation) and typically have similar, sometimes better, power. Three factors are however critical in making optimal use of such methods: access to good data on cases, computerised vaccination records with the ability to link them to cases and availability of appropriate analysis techniques.
Several studies on vaccines have compared traditional and case-only study designs:
In situations where primary data collection is needed (e.g. a pandemic), the SCCS may not be timely 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 Safety 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 used in Guillain-Barré syndrome and adjuvanted pandemic influenza A (H1N1) 2009 vaccine: multinational case-control study in Europe. BMJ 2011;343:d3908).
Ecological analyses should not be considered hypothesis testing studies. See section 5.5. of this Guide.
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.
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.
Observational studies on vaccine adverse effects during pregnancy (especially on pregnancy loss), which often use pregnancy registries or healthcare databases, are faced with three challenges: embryonic and early foetal loss are often not recognised or recorded, data on the gestational age at which these events occur are often missing, and the likelihood of vaccination increases with gestational age whereas the likelihood of foetal death decreases. Assessing the effect of vaccine on spontaneous abortion using time-dependent covariates Cox models (Pharmacoepidemiol Drug Saf 2012;21(8):844-850) 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. 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.
Few vaccine studies are performed in immunocompromised subjects. 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. Further research is needed on this topic.
|10. Specific topics|
|Annex 1.||Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes|