A framework for research on vaccine effectiveness

Vaccine xxx (2018) xxx–xxx

Contents lists available at ScienceDirect

Vaccine journal homepage: www.elsevier.com/locate/vaccine

Review

A framework for research on vaccine effectiveness Natasha S. Crowcroft a,b,⇑, Nicola P. Klein c a

Applied Immunization Research and Evaluation, Public Health Ontario, 480 University Avenue, Suite 300, Toronto, ON M5G 1V2, Canada University of Toronto, 480 University Avenue, Suite 300, Toronto, ON M5G 1V2, Canada c Kaiser Permanente Vaccine Study Center, 1 Kaiser Plaza, 16th Floor, Oakland, CA 94612, United States b

a r t i c l e

i n f o

Article history: Received 11 December 2017 Received in revised form 7 March 2018 Accepted 4 April 2018 Available online xxxx Keywords: Immunization Vaccine effectiveness Vaccine program evaluation Vaccine study design Vaccine failure

a b s t r a c t The need for a systematic approach to research on vaccine effectiveness (VE) is increasing with growing numbers of vaccines and complexity of immunization programs. The diverse scientific fields that investigate how vaccines work and why they fail continue to evolve, yet definitions related to such advances have not kept pace. Researchers in disciplines ranging from basic science through immunopathology, clinical and epidemiological research, and mathematical modelling need more precise definitions to promote communication and interdisciplinary VE research to ensure that studies are designed to appropriately address relevant questions. To meet these aims, we suggest standardized definitions, consider models of vaccine failure, and offer general approaches for incorporation into study design. We further propose a framework for conducting VE research that builds on the traditional epidemiological triad of host, pathogen and environment, and also includes additional elements such as characteristics of both the vaccine and vaccinee, the effect of time on likelihood of exposure and protection, and the impact of environment and pathogen, as well as how outcomes of interest and study design may impact observed vaccine effectiveness. The framework is relevant to researchers in all disciplines who investigate the effectiveness of vaccines and vaccination programs and why they may fail. Stronger research in this field will help policy makers optimise decision-making on vaccination programs, ensuring we maximize the health benefits of vaccines. It is also important for clinicians communicating the benefits of vaccines to the public. Crown Copyright Ó 2018 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Contents 1. 2.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vaccine failure models (Table 1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Primary vaccine failure (‘‘All-or-None”) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Secondary vaccine failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Exposure threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. Leaky vaccine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Multimodal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vaccine effectiveness framework considerations (Table 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Characteristics of the vaccine recipient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. The vaccine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Environment and exposure to the pathogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. The pathogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Outcome of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7. Study design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations: aP, acellular pertussis vaccine; VE, vaccine effectiveness; wP, whole – cell pertussis vaccine.

⇑ Corresponding author at: Applied Immunization Research and Evaluation, Public Health Ontario, 480 University Avenue, Suite 300, Toronto, ON M5G 1V2, Canada. E-mail address: [email protected] (N.S. Crowcroft). https://doi.org/10.1016/j.vaccine.2018.04.016 0264-410X/Crown Copyright Ó 2018 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: Crowcroft NS, Klein NP. A framework for research on vaccine effectiveness. Vaccine (2018), https://doi.org/10.1016/j. vaccine.2018.04.016

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Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Funding sources and potential conflicts of interest . Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction Understanding how well vaccines work matters to a wide range of professionals, including clinicians advising patients, public health epidemiologists and modellers evaluating programs, policy makers making decisions, and basic researchers investigating vaccine immunity and designing better vaccines. An increasing number of vaccines are currently given to entire healthy populations of children, pregnant women and adults. Yet debate about the benefit of vaccination, such as influenza vaccines, shows that the results of vaccine effectiveness (VE) research impact public confidence [1]. Vaccines are typically very effective but rarely provide permanent and complete protection from infection. Evaluating vaccine protection under real-world conditions is becoming increasingly complex and the traditional dichotomous paradigm of VE is no longer an adequate conceptual basis for this research. Definitions related to VE research have not kept pace with our growing understanding of how vaccines work and why they fail. Note that terms are used inconsistently in the literature causing potential confusion, sometimes using ‘‘vaccine efficacy” (protection observed in controlled experimental conditions) to refer to vaccine effectiveness (the proportional reduction of infection in a realworld immunization program delivered with normal storage and administration processes to an unselected population) [2–4]. Researchers in disciplines ranging from basic science through immunopathology, clinical and epidemiological research, and mathematical modelling need more precise definitions to ensure that VE studies are designed to best address relevant questions. Better language will in turn promote better communication and interdisciplinary VE research. For example, epidemiologists have been questioning the impact of vaccines on pertussis transmission for decades, but only recently have basic science researchers begun to address this question. To promote more interdisciplinary communication and stronger research on VE, here we propose updated definitions for vaccine failure and a conceptual framework for evaluating and interpreting VE. We iteratively developed the vaccine failure concepts and definitions (Table 1). Since the natural history of vaccine failure

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should determine how to study VE, we consider existing vaccine failure models and propose additional models as needed [5]. These definitions then helped inform the broader implications for study design and led to our proposed VE framework (Table 2). This VE framework builds on the traditional epidemiological triad of host, pathogen and environment, and includes additional elements to consider (as discussed below). Although important, we do not discuss vaccine program effectiveness (where this is defined as the change in disease burden at population-level attributable to the implementation of an immunization program [6]). 2. Vaccine failure models (Table 1) 2.1. Primary vaccine failure (‘‘All-or-None”) Vaccine protection and failure are two sides of the same coin, but understanding how vaccines fail is a relatively underexplored area. Traditionally, vaccines were thought to generate life-long immunity, with a small proportion of vaccinees not protected because the vaccine did not ‘‘take” (‘‘all-or-none”). This is considered ‘‘primary vaccine failure” and is frequently associated with live virus vaccines such as measles, mumps and rubella vaccines [7]. 2.2. Secondary vaccine failure In contrast, ‘‘secondary vaccine failure” refers to waning vaccine immunity in which protection decays with time. For example, in the absence of circulating pathogen, humoral protection might be expected to wane exponentially [8,9]. Secondary failure has traditionally been more associated with inactivated, subunit, and toxoid vaccines (e.g., pertussis, diphtheria and tetanus). 2.3. Exposure threshold The immune system is a dynamic non-linear complex system and pathogen exposure could either boost immunity or result in

Table 1 Definitions. Vaccine efficacy, effectiveness and failure Vaccine efficacy is the proportional reduction of infection in a vaccinated group compared with an unvaccinated group under optimal conditions such as a randomized controlled trial Vaccine effectiveness (VE) is the proportional reduction of infection in a real-world immunization program delivered with normal storage and administration processes to an unselected population Vaccine failure is the occurrence of infection or disease in an individual who is fully vaccinated Primary vaccine failure is the occurrence of infection or disease in a fully vaccinated individual who failed to make an immune response to the vaccine Secondary vaccine failure is the occurrence of infection or disease in a fully vaccinated individual who made a normal immune response to the vaccine (which may or may not have been measured) but whose immunity has subsequently waned. Definitions of mode of action and/or failure of vaccines All-or-none vaccines (Primary vaccine failure): infection has age-appropriate severity Antibody-mediated with exponential waning (Secondary vaccine failure): antibody-mediated protection declines exponentially (severity may increase inversely) ‘‘Exposure threshold” vaccines in which VE depends on the dose of exposure and is lower following high dose exposure than low dose exposure ‘‘Leaky vaccines” in which each exposure carries an equal risk of infection for everyone, with no change in severity; may look like waning after multiple exposures Multimodal in which multiple modes of action (and/or vaccine failure) co-exist Vaccine modified disease in which symptoms and outcome may be milder (or rarely more severe) in vaccinated than unvaccinated Interruption of transmission is a feature of herd immunity (or ‘‘indirect effects of immunization”) that occurs through reduced asymptomatic infection and/or carriage and/or lower or no circulation of the pathogen.

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Table 2 Vaccine effectiveness framework. 1.     

Model of vaccine failure All-or-none vaccines Antibody-mediated with exponential waning ‘‘Exposure threshold” vaccines ‘‘Leaky vaccines” Vaccine modified disease

2.       

Characteristics of vaccine recipient Vaccination status: Priming type of vaccine, boosting vaccine type, total number of previous doses, date of administration, time period between doses Age at receipt, age at priming, age at boosting, age at time of study Co-administered vaccines and drugs Age at time of study, comorbidity, immune disorders, nutritional status, immune hypo-responsiveness, time of day of vaccination Previous history of infection For immunization in pregnancy, maternal characteristics and infant characteristics (e.g. prematurity) Socio-demographic characteristics – e.g. contact with young children, healthcare provider, social determinants of health

3.     

Vaccine Each type of vaccine and formulation (e.g. aP/wP, DTaP/TdaP) used for all priming and boosting doses Manufacturer for each vaccine dose, vaccine manufacturer lot numbers Mechanism of protection – e.g. type of immunity achieved (e.g. type 1 or 2 T helper cell mediated immunity, etc.) Natural history model of vaccine failure and protection Site, route, dose of vaccination

4.     

Time When was the study conducted? Epidemic or non-epidemic period? Other secular trends? Time between doses Time since vaccination (waning) Model of decay (e.g. Exponential) of humoral immunity and cell-mediated immunity How are age and time-varying covariates being assessed?

5.    

Environment and exposure to pathogen Indirect/herd effects Intensity of exposure to the pathogen, setting of exposure Comparability of likely exposure to infectious pathogen in cases and controls Boosting effects of circulating pathogens

6. Pathogen  What infection is being prevented? o Subgroups (conjugate vaccines), specific strains (polio), strain match (influenza)  Laboratory diagnostic methods used to identify cases (culture, PCR)  Vaccine-driven evolution e.g. vaccine strain type (thought to affect the evolution of pertactin-negative pertussis strains)  Natural history of infection and protection/immunity  Co-infection 7.   

Outcomes of interest What are the goals and objectives of the program? What is the target group – whole population or individual protection of high risk groups? Protection of unborn children (Congenital Rubella Syndrome)? Which outcomes are being studied? o Asymptomatic and/or carriage o Typical disease, atypical disease, reinfection, mild infection, severe infection, deaths o Direct impact in the vaccinated individual or indirect effects? o Impact on infectiousness and transmission o Protection of newborn (maternal immunization)  What is included in the case definition(s)?

8. Study Design considerations  Choosing appropriate controls is one of the most important factors to consider and the choice may vary depending on the model of failure.  Study design and analysis should also consider a wide range of elements including case ascertainment, sensitivity and specificity of case definition, potential bias and confounding including health care seeking behaviour, social determinants, exposure to prophylaxis, diagnostic method (culture, PCR), infectiousness, and risk of misclassification

infection, depending on the level of immunity at the time of exposure. Similarly, thresholds of protection are not necessarily fixed; toxoid vaccines against tetanus will fail despite antibody levels being above protective levels if toxin exposure is high enough [10]. We describe this as an ‘‘exposure threshold” mechanism of failure. Conversely, vaccines may not fail despite antibody levels being below protective levels if there are memory cells for diseases with long incubation period, which happens following hepatitis B virus exposure, or if other mechanisms such as cell-mediated immunity are critical to protection. The role of cell-mediated immunity is being increasingly recognized as important not just for the prevention of viral infections but also of bacterial infections such as pertussis, but is challenging to study because cellular immunity is dynamic and the tools for its assessment are more complex than those developed for measuring humoral immunity.

2.4. Leaky vaccine Another model, the ‘‘leaky” vaccine, refers to a reduced probability of infection in all recipients, and that risk of infection, given exposure, remains constant over time. This model, so far only described for malaria vaccines and theorized for other vaccines [11], has proved useful for conceptualizing how multi-stage pathogens might evade the immune system at different stages [12]. Mathematically, since the proportion of people who become infected increases continuously with time since vaccination, protection afforded by leaky vaccines may be misinterpreted as waning. 2.5. Multimodal However, we increasingly recognize that there are multiple models of vaccine failures and the model may differ depending

Please cite this article in press as: Crowcroft NS, Klein NP. A framework for research on vaccine effectiveness. Vaccine (2018), https://doi.org/10.1016/j. vaccine.2018.04.016

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on the outcome. For example, mumps and varicella vaccines, although live virus vaccines, demonstrate both primary and secondary vaccine failure models, and waning immunity may be the most important of these. In the case of pertussis, while it is clear that protection wanes, several models may also be needed to describe its vaccine failure mechanism. In comparing whole cell pertussis (wP) versus acellular pertussis (aP) vaccines, humoral protection is important, but correlates of protection have not been determined. As noted above, cell-mediated immunity is critical for determining the extent and duration of pertussis protection, however it is challenging to study in humans [13–15]. Thus, because multiple components contribute to immunity, differing models that wane at different rates may be needed. Furthermore, factors contributing to protection against severe pertussis are likely different from those that prevent transmission, and this needs to be considered in research design. Overall, the natural history model for specific vaccines in every setting may not be known and different models could apply at different times

3.3. Time VE fundamentally varies over time. At least two distinct timevarying aspects need to be taken into account when designing and analyzing VE studies [28]. Firstly, infectious diseases are typically dynamic, resulting in time-varying exposure. To ensure that both cases and controls had the same likelihood of exposure to the infection (i.e., whether or not it was an outbreak period), analyses must control for calendar time. Secondly, analyses need to consider time since vaccination in relation to duration of protection [29,30]. Several different analytic approaches have been taken to assessing waning immunity and other factors that may change over time, including stratification of VE by time since last dose of vaccine, time series analysis, Cox regression analysis [31] survival analysis [3], fixed effects parametric models [32], regression continuity [33] and frailty mixing models [34]. This list is not exhaustive, but rather to highlight that time needs to be carefully considered when designing VE studies. 3.4. Environment and exposure to the pathogen

3. Vaccine effectiveness framework considerations (Table 2) 3.1. Characteristics of the vaccine recipient In addition to the models of vaccine failure, VE is influenced by many characteristics of the recipient, including, age at vaccination, age at exposure to infection, prior and co-administrated vaccines, and underlying health status. Co-morbidities which influence VE include immune-modulating conditions and medications [16], prematurity, and underlying immune deficiencies [17], as well as potential effect modifiers such as ethnicity, sex, pregnancy, and potentially time of day the vaccine was administered [18]. Age is a particularly important determinant of VE. Infants lack T-cell independent responses and will often require multiple doses, and protection wanes quickly for example, if children are vaccinated as infants, the effectiveness of meningococcal serogroup C vaccine is negligible by the time they reach 1 year of age [19]. Multiple doses of meningococcal conjugate vaccines are needed in infants, although a single dose will provide longer lasting protection in children over 12 months [20]. Similarly, the capacity of the immune system to respond to antigens fades qualitatively and quantitatively later in life [21]. Many vaccines require multiple doses and are often given in combinations that change over time, thus accurately classifying individual vaccination status requires access to complete medical records. Such high quality individual level vaccine data are often lacking, potentially resulting in vaccine status misclassification and bias in VE analyses.

3.2. The vaccine Variations in vaccine formulation including vaccine strain antigen amount, site of administration and the presence of adjuvants can all affect VE [22]. Most vaccines are administered in combination vaccines and each component may influence the effectiveness of the others [23]. For wP vaccines, VE varies widely between manufacturers. The Canadian Connaught wP had low effectiveness [24] compared with the UK Evans product [25]. In contrast, aP formulations are more consistent, but wane more rapidly than wP [26]. The negative impact of aP priming on VE continues in teenagers who previously received only acellular vaccines during infancy and childhood [19,27,28].

Although VE studies may define vaccine receipt as ‘‘exposure” (akin to pathogen exposure), immunization is more correctly a host modifier that intervenes between pathogen exposure and outcome. Considering how pathogen exposure impacts the traditional host, pathogen, disease triad is important for the design of VE studies. It is not usually possible to directly measure pathogen exposure, which may vary by age of the infected source (younger children being more infectious for example), vaccination status, time period (outbreak versus non-outbreak), and population mixing patterns. Household transmission and institutional outbreak studies have attempted to partially overcome this limitation by assuming an even distribution of pathogen exposure. VE studies need to distinguish indirect effects of vaccination, such as the decrease in adult pneumococcal disease following routine infant vaccination with pneumococcal conjugate vaccines, from direct effects [35]. Failure to take account of indirect effects can lead to misleading VE estimates because unvaccinated controls in the same community will have less pathogen exposure, which will make the effectiveness of vaccination appear to be lower. Direct effects can sometimes be estimated from clinical trials if the studies are small enough for indirect effects to be negligible. Once a program is rolled out in a population it may become very difficult to disentangle direct and indirect effects because the benefits to vaccinated individuals will generally be a combination of both effects. An indication may be given by comparing the incidence in vaccinated individuals with contemporaneous or historical unvaccinated communities that are similar to the vaccinated. Estimating indirect effects may be easier than direct effects, the most straightforward approach being to examine the incidence of disease in unvaccinated groups such as those too young, too old, or not targeted for the program, or observing changes to the average age at infection. If an immunization program’s impact on the incidence of disease is greater than the vaccination coverage, this is a good indication that indirect effects are an important component of the impact of that program [36]. 3.5. The pathogen The natural history of infection and resulting immunity often determines what VE can be expected from a vaccine. For example, hepatitis A infection result in long-lasting immunity to all hepatitis A viruses and such long-term protection is mirrored in the high and durable effectiveness of hepatitis A vaccines [37]. Similarly, diphtheria toxoid vaccines prevent the toxin-mediated disease by generating anti-toxin antibodies which mimic those that would

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follow natural disease. Where a pathogen has multiple serogroups or types, such as encapsulated bacteria including meningococcus and pneumococcus, or human papillomavirus, effectiveness of a vaccine may be constrained to specific serogroups, types or strains. Similarly, pathogens which undergo in-host evolution can present serious challenges to vaccine development, as seen in the case of human immunodeficiency virus. Some pathogens have complex patterns of strain evolution and occurrence in which past immunity impacts response to vaccination, as seen for example in the cases of influenza and dengue fever. For influenza, prior immunity from either vaccination or infection can reduce the immune response to subsequent immunization (a phenomenon that is sometimes referred to ‘‘original antigenic sin” or ‘‘antigenic seniority”) [38]. In the case of dengue vaccine, past infection is associated with better protection from vaccination, whilst immunologically naïve individuals may have increased severity of infection following vaccination [39]. 3.6. Outcome of interest Clinical case definitions, ranging from severe disease through to asymptomatic infection and carriage [40,41], are used to measure

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outcomes and can greatly affect what VE is observed. The use of both a stringent World Health Organization (WHO) pertussis case definition requiring at least 21 days of cough [42,43] and a more sensitive WHO surveillance case-definition [44] overestimated the VE of aP vaccines because pertussis vaccination is less effective against laboratory-confirmed milder infections that did not meet either case definition; including these milder cases lowered the VE estimates. Mild cases in vaccinated individuals are also referred to as ‘‘vaccine modified disease” (could also be considered as ‘‘partial protection”). Conversely, case definitions that do not include laboratory confirmation may under-estimate VE because of nondifferential misclassification of cases [45]. Finally, the outcomes may not be in the vaccinees themselves, but rather in those who are protected. For example, the effectiveness of maternal tetanus or pertussis immunization should be assessed in their infants. Likewise, reduced infectiousness among vaccinated cases resulting in decreased spread should be assessed in contacts of cases. The goals of immunization programs, while not directly affecting VE study designs, determine the main outcomes of interest. The goal for HPV vaccination in Canada was to reduce cervical cancer only [46], In contrast, in the journey towards polio eradication, strain-specific VE estimates have played important roles in

Table 3 Some Methodologic Considerations for Different Study Designs. Design

Methodological considerations

Outbreak investigation

VE is anticipated to be lower during outbreak than non-outbreak periods under the leaky, exposure threshold or waning immunity models. During outbreaks, cases and controls may be more likely to have similar opportunities for exposure to infection, and detection of milder cases may be better because of enhanced public awareness and decreased provider and patient bias against testing vaccinated cases. This may explain why lower, and arguably more accurate, VE estimates are found than in non-outbreak periods. Case-control VE studies usually yield similar results to cohort studies [60]. However, for appropriate comparability cases and controls both need to have equal chance of exposure to infection, the same likelihood of disease detection, and unbiased record of vaccination status. VE can be over-estimated in non-epidemic periods. For example, a matched casecontrol study in Japan during a non-epidemic period found pertussis VE ranged between 96.9 and 95.9% [61], while during an outbreak, VE was substantially lower (78%) [62]. If disease attack rates are high in vaccinated, VE may be overestimated [63]. In the indirect cohort design (‘‘Broome” method) [64,65] the control group consists of individuals identified through the same system as the cases and with the same disease but who have a non-vaccine type of infection, such as a non-vaccine serogroup of pneumococcal disease or a non-vaccine HPV strain. Test-negative designs (TND) [66] have been most frequently conducted for influenza VE studies [67], for which they have been validated [67–70]. Cited strengths are that controls are comparable for healthcare seeking behaviour and disease exposure. However, test-negative controls risk not being representative of cases, and false-negative cases may appear amongst controls, both of which are conservative selection biases in the direction of under-estimating VE. Test-negative influenza VE studies are also vulnerable to ‘‘collider bias”, where test negative individuals could differ from cases because they are selected on the basis of being tested, but testing is also linked to likelihood of having influenza, of being vaccinated, and other confounding factors [71,72]. TND studies of pertussis have a few advantages over influenza studies in that almost everyone has been vaccinated and fewer alternative pertussis-like-illnesses exist [73,74]. Although older cases may present for care later, have milder disease and have a negative culture or PCR result [75], misclassification bias analysis has supported using TND for pertussis [19].VE may be lower in a TND versus population-based controls because of: (1) adjustment for healthcare seeking behaviour (2) more comparable exposure to disease (3) conservative bias (if over-matching) Household VE studies may help ensure that pathogen exposure is similar for cases and controls, although even within households there is heterogeneity in infectivity and contact between members [76]. Such design may detect differential effects between individuals with close physical contact (e.g. mother/child), but may be less able to distinguish amount from frequency of pathogen exposure. Exposure is likely to be higher and more frequent in a household setting. Observed VE is likely to be lower than in other settings if the vaccine failure model is leaky (because of multiple exposures) or exposure threshold (because of intense exposures). VE estimates could be more accurate because exposure and case ascertainment is more comparable in cases and controls. Akin conceptually to household studies, recent studies asses VE of maternal immunization against infant disease. In this approach, each mother and baby pair is well matched to each other for disease exposure and maternally-derived immunity passed to the infant. However, the impact of prenatal immunization needs to be distinguished from that of the infant’s vaccines, either by restricting VE assessments to time before the infant receives any vaccines or by adjusting the analyses for subsequent infant doses [77]. If feasible, infant’s immune response to vaccination should be measured. It is important to ensure complete collection of exposure, potential confounders and effect modifiers as well as unbiased data collection for vaccination status and disease. Mathematical modelling studies may be helpful adjuncts to observational data. They may use age-varying mixing behaviour as a proxy for exposure and risk of transmission [78]. The screening method is a useful approach when good data on vaccine status of cases and immunization coverage of the population are available. The approach is unreliable and may over-estimate VE if coverage is either very high or overestimated, and underestimates VE if the number of vaccinated cases is underestimated. When data are readily available, it can indicate need for more rigorous study [79].

Unmatched case control study with population based controls

Matched case control study Indirect cohort design

Test negative case control study

Household study

Maternal immunization

Cohort study Mathematical modelling Screening method

Abbreviations: Test Negative Designs TND; Vaccine Effectiveness VE.

Please cite this article in press as: Crowcroft NS, Klein NP. A framework for research on vaccine effectiveness. Vaccine (2018), https://doi.org/10.1016/j. vaccine.2018.04.016

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decisions on when it is safe to switch to different inactivated polio vaccine formulations with lower VE, and to discontinue oral polio vaccines [47]. 3.7. Study design considerations Standard study designs do not necessarily reveal the underlying model of vaccine failure and need to be purposefully designed to reveal which mechanism(s) are at play. In general, infectivity and number of exposures to an infectious contact is not usually measurable, with some exceptions [48]. All study designs which measure VE have strengths and weaknesses (Table 3). Given the huge amount of literature on bias and confounding in epidemiology, here we aim not to review the methodology comprehensively but to highlight key considerations, hoping to encourage overall better designed studies. VE studies triggered by outbreaks illustrate many of the strengths and weaknesses of VE study design. VE field studies often present a host of challenges [49] including a lack of high quality vaccine and clinical data. Typically, lower VE is observed during outbreaks than during non-outbreak periods [50], which may be caused by biases such as increased case ascertainment of milder cases and vaccine-modified disease, or because VE is truly lower than previous estimates because of a local issue with a specific vaccine, for example. New approaches to studying VE are being developed, applied to new pathogens or applied in new settings. Examples include cluster randomized control trials, to compare different influenza vaccines in nursing homes [51] and ring vaccination clusterrandomized trial to assess Ebola vaccination [52]. Newer statistical methods include applying Bayesian methods [53]. Finally, challenge studies may help to unravel the patho-immunology and control exposure, but are rarely conducted in humans [54]. 4. Discussion To ensure high quality VE studies, researchers and public health practitioners need to think carefully about many elements, including models of vaccine failure [3]. VE studies need to carefully take biases into account when being designed. There may be no best design for a VE study, but an agreed hierarchy of quality of evidence would help improve how knowledge is synthesized and interpreted and clarify variations such as different study design inclusion criteria [55–57]. We have developed a framework that we hope will facilitate better communication and alignment between basic and field research and promote more high quality VE studies. It was not possible within the scope of an overview to go into detail on every element; we have focused on the gaps given that detailed literature exists on pathogens, vaccines, study design, bias and confounding. Increasing the number of welldesigned and executed studies will contribute to better understanding of how vaccines work and increasing robustness of the evidence. Ensuring that all the key factors are considered and reported in VE studies may require a new STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement for VE, analogous to the recently published STROBEInfectious Diseases (STROBE-ID) [58]. The framework we present in Table 2 could be readily adapted for this purpose. The implications of our growing understanding of the complexity of this field go beyond public health practitioners conducting field investigations to the need for inter-disciplinary approaches, applying standardized concepts and using an agreed-upon terminology. Better understanding of the natural history, kinetics and immunology of vaccine failure needs to be applied in a variety of different approaches. High quality detailed epidemiological

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