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Am J Epidemiol. 2008 December 15; 168(12): 1343–1352.

Published online 2008 October 29. doi: 10.1093/aje/kwn259

PMCID: PMC2638553

Correspondence to Dr. Ira M. Longini, Jr., Program in Biostatistics and Biomathematics, Vaccine and Infectious Disease Institute, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, Seattle, WA 98109 (e-mail: gro.prahcs@inignol).

Received 2008 April 24; Accepted 2008 July 28.

Copyright American Journal of Epidemiology © 2008 The Authors

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been cited by other articles in PMC.

In this paper, the authors provide estimates of 4 measures of vaccine efficacy for live, attenuated and inactivated influenza vaccine based on secondary analysis of 5 experimental influenza challenge studies in seronegative adults and community-based vaccine trials. The 4 vaccine efficacy measures are for susceptibility (VE* _{S}*), symptomatic illness given infection (VE

A single measure of vaccine efficacy fails to capture the multidimensional protective effect of vaccination. Individual vaccination can prevent or reduce a number of outcomes, including laboratory-confirmed infection, symptomatic illness given infection, infectivity of infected individuals, or a combination of these. Vaccine efficacy (VE) is a measure of relative risk (RR) that generally takes the form VE=1 − RR. The absolute efficacy of a vaccine compares relative risk in a vaccinated group with that in a control group. For the relative efficacy of one vaccine compared with another formulated against the same infectious agent, the relative risk is compared between 2 different groups receiving the 2 different vaccines against the same pathogen (refer to the Appendix).

Previously, Halloran et al. (1) defined several key vaccine efficacy parameters necessary to evaluate the ability of a vaccine to reduce infection, symptomatic illness, and infectivity. Both vaccine efficacy for susceptibility (VE* _{S}*) and vaccine efficacy for infection-confirmed symptomatic illness (VE

Vaccine efficacy for illness given infection (VE* _{P}*) and vaccine efficacy for infectiousness (VE

Currently, both inactivated influenza vaccine and live, attenuated influenza vaccine are administered in the United States yearly to reduce the morbidity and mortality associated with seasonal influenza. Yet, there is a need to estimate the multidimensional measures of vaccine efficacy described for both of these influenza vaccines.

In this paper, we estimate VE* _{S}*, VE

To identify recent, relevant influenza challenge studies, we conducted a search of publications indexed in PubMed (National Library of Medicine, National Institutes of Health, Bethesda, Maryland). The search criteria consisted of the following terms: (influenza, human *and* influenza vaccine) *and* (experiment* *or* challenge* *or* “wild type” *or* wildtype). We limited the search to articles published in English between January 1, 1980, and January 1, 2008, and indexed as research conducted in humans to obtain results. A total of 231 articles were returned in early 2008. In addition, influenza experts were consulted to seek out any additional influenza challenge studies.

To be included in this analysis, studies had to be randomized, controlled trials involving an experimental influenza challenge in human subjects. At least 2 of the following groups were required for comparison: 1) participants receiving live, attenuated influenza vaccine; 2) participants receiving inactivated influenza vaccine; and 3) controls receiving placebo or no vaccine at all. All study participants had to be seronegative for the influenza challenge strain (defined as serum hemagglutination-inhibition antibody titer of ≤1:8) prior to vaccination. If not all participants were seronegative, data for a seronegative subgroup had to be available. The dosage of live vaccine had to exceed 10^{7} 50% tissue culture infectious dose, the level used in licensed live vaccine. The challenge had to occur at least 2 weeks postvaccination, and the type of challenge strain and type of vaccine strain administered had to be identified. Furthermore, we required that the challenge strain be a wild-type virus (not a vaccine strain) to more closely resemble natural infection. The data presented had to include the outcomes of interest (laboratory-confirmed influenza infection, viral shedding, and/or any influenza-like illness among infecteds) and provide enough detail to be able to estimate the vaccine efficacy parameters included in this secondary analysis. Each of 231 abstracts was reviewed to determine whether the inclusion criteria were met. The full-text articles for all abstracts that appeared to describe an influenza challenge study were then reviewed in detail. Any uncertainties about whether a study qualified were discussed by 2 of the authors and were resolved. In total, 5 studies met all of the inclusion criteria and were included in this analysis (2–6). For 1 study (6), a subset of the data reported in the manuscript that contained only seronegative volunteers was analyzed in accordance with the inclusion criteria.

Information about the sample size, treatment groups, type of influenza vaccine strain, type of influenza challenge strain, challenge strain dose, and time between vaccination and challenge was abstracted from each article, along with the number of participants in each treatment group for each of the outcomes. With these data, we calculated the following: 1) the absolute efficacy of live, attenuated vaccine; 2) the absolute efficacy of inactivated vaccine; and 3) the relative efficacy of live, attenuated vaccine compared with inactivated vaccine for each of the 4 vaccine efficacy measures described above (VE* _{S}*, VE

To estimate VE* _{S}* and VE

Because the upper bound for positive efficacy estimates is 1 but the lower bound for negative efficacies is −∞, each of the negative efficacy estimates was corrected. In the equation VE=1 − RR, the reciprocal of the relative risk was used in place of the relative risk, and the resulting difference was multiplied by (−1). To summarize the individual vaccine efficacy estimates, we calculated weighted averages for each efficacy measure by using the inverse of the variance as the value of the weight. In addition, 95% confidence intervals weighted by the inverse of the variance for each of these summary measures were calculated by using large-sample asymptotic methods.

We reviewed the literature to identify several recent community-based influenza vaccine trials that used culture- or serologically confirmed influenza outcomes or validation sets to report at least 1 measure of vaccine efficacy. We categorized each reported measure of vaccine efficacy from the 11 studies identified according to the specific measure of efficacy to which it corresponded on the basis of the outcomes that the study recorded. A brief summary of these studies is presented in this paper.

We develop the composite vaccine efficacy measure, VE* _{C}*, similar to Halloran et al. (7), that measures how all the vaccine effects—VE

The treatment groups available for comparison (including the type of influenza vaccine strain), the data for each outcome, the challenge dose, and the time interval between vaccination and challenge are provided in Table 1 for each of the studies analyzed. All studies identified were carried out among adult volunteers. In each study, participants were challenged with a wild-type strain of influenza virus homologous to 1 of the strains contained in the vaccine that they had received. The time between vaccination and challenge ranged from 4 weeks to 7 months.

Data From the Experimental Influenza Challenge Studies Used in the Analysis of Influenza Vaccine Efficacy

Figure 1A–C presents both the point estimates for VE* _{S}*, VE

Weighted Mean Vaccine Efficacy Estimates and 95% Confidence Intervals From a Secondary Analysis of 5 Experimental Influenza Challenge Studies in Adults^{a}

Point estimates and the weighted mean for the A) absolute efficacy of live influenza vaccine based on secondary analysis of the influenza challenge study data, B) absolute efficacy of inactivated influenza vaccine based on secondary analysis of the influenza **...**

The VE* _{S}* point estimates for the absolute efficacy of live (VE

The VE* _{P}* for the absolute efficacy of live vaccine was VE

Live vaccine appeared to offer modestly better protection against laboratory-confirmed influenza illness (VE* _{SP}*=77%, 95% CI: 27, 100) when compared with a control group than inactivated influenza vaccine did (VE

The point estimates for the absolute efficacy of the vaccine in reducing viral shedding (VE* _{I}*) were low for both live, attenuated vaccine (VE

Community-based vaccine trials often report various measures of vaccine efficacy depending upon the specific outcome identified in the study, whether it is laboratory-confirmed infection, illness given infection, laboratory-confirmed influenza illness, or infectivity among infecteds. In this paper, we categorize community-based vaccine efficacy studies based on the specific component of vaccine efficacy that was reported: VE* _{S}*, VE

Evidence from community-based vaccine trials indicates that live, attenuated vaccine provides significantly better protection than inactivated vaccine against laboratory-confirmed influenza illness in children. In a randomized, double-blind comparison of live, attenuated and inactivated vaccine administered to 7,852 children aged 6–59 months during the 2004–2005 flu season, when 1 of the circulating influenza strains was homologous to the vaccine strain, VE* _{SP}* for the relative efficacy of live compared with inactivated vaccine against culture-confirmed influenza-like illness was 45% (95% CI: 22, 61) for well-matched influenza strains (11). An earlier randomized, double-blind trial among 2,187 children aged 6–71 months with a history of recurrent respiratory infections reported similar results when the circulating influenza strain was also homologous to the vaccine strain (VE

Several studies also reported a high VE* _{SP}* for the absolute efficacy of live vaccine in children. An analysis of the double-blind, randomized controlled trial conducted among 1,602 children aged 15–71 months by Belshe et al. (13, 14) found that the VE

In general, trials reporting vaccine efficacies for circulating influenza strains heterologous to the vaccine strains provided lower estimates than those reporting efficacy estimates against homologous strains. Two of the studies reported the absolute efficacy of live vaccine against heterologous strains. One study reported the absolute efficacy of live vaccine against antigenically dissimilar strains as VE* _{SP}*=48%, 95% CI: −11, 76 (16), and 1 reported a VE

Not all estimates for the efficacy of live vaccine against heterologous strains were low, however. A double-blind, randomized controlled trial of 1,358 children aged 26–85 months reported a VE* _{SP}* for the absolute efficacy of live vaccine against heterologous strains as 89% (95% CI: 81, 94) (14, 19), which was similar to the efficacy against homologous strains discussed above (14). In a randomized, double-blind comparison of live, attenuated vaccine with inactivated vaccine in children, the authors reported a relative efficacy against culture-confirmed influenza illness caused by poorly matched strains that was higher than that estimated for well-matched strains (VE

A true estimate of VE* _{I}* is difficult to obtain; as a result, VE

Fewer recent studies have reported the efficacy of influenza vaccine in adults. In a community-based trial of 1,247 healthy adults randomized to receive live, attenuated vaccine, inactivated vaccine, or placebo during the 2004–2005 influenza season, when a drifted strain was circulating, Ohmit et al. (21) estimated the VE* _{SP}* against culture- or serologically confirmed infection and illness for the absolute efficacy of inactivated vaccine as 67% (95% CI: 16, 87) and for the absolute efficacy of live vaccine as 30% (95% CI: −57, 67).

As would be expected, the sensitivity of the laboratory methodology used to confirm infection affects the estimates of vaccine efficacy. In addition to reporting the vaccine efficacy against culture- or serologically confirmed influenza illness, Ohmit et al. (21) also reported the efficacy of live and inactivated vaccine against culture-positive, polymerase chain reaction–positive, culture- or polymerase chain reaction–positive, and serologically positive infection and illness. The estimates for the absolute efficacy (VE* _{SP}*) of live vaccine ranged from 28% (95% CI: −67, 67) to 57% (95% CI: −3, 82). The estimates for the absolute efficacy (VE

Figure 2A shows the contour lines for values of VE* _{SP}* as a function of VE

Vaccine efficacy for laboratory-confirmed influenza illness (VE_{SP}) and combined vaccine efficacy (VE_{C}) as functions of vaccine efficacy for susceptibility (VE_{S}), vaccine efficacy for illness given infection (VE_{P}), and vaccine efficacy for infectiousness **...**

Table 3 gives our expected vaccine efficacies for live and inactivated seasonal influenza vaccine in seasons when homologous and heterologous strains are circulating based on our best guesses from the information presented in this paper. We used the relative efficacy with VE* _{SP}*=50% when comparing live with inactivated vaccine. We assumed that VE

This analysis demonstrates the feasibility of estimating 4 components of vaccine efficacy simultaneously by using existing influenza challenge study data. Detailed, accurate, and reliable outcome data are needed to calculate these measures of vaccine efficacy with precision, and steps should be taken to incorporate the necessary data collection into the design of vaccine field trials, as noted before (1). In addition, our classification of vaccine efficacy measures from community-based vaccine trials highlights additional ranges of efficacy estimates observed and the importance of specifying the exact component of vaccine efficacy that is being reported, both to assess comparability between studies and to facilitate a more thorough understanding of the components of vaccine efficacy.

We do not know of any community-based influenza vaccine trial that has provided estimates of all 4 vaccine efficacy components or of VE* _{I}*. It would be beneficial to design future phase III vaccine trials and phase IV vaccine studies to estimate all 4 components of vaccine efficacy. Better infection outcome measures could be used to separately estimate VE

Our estimates based on challenge study data indicate that live, attenuated influenza vaccine, as well as inactivated influenza vaccine, protected against influenza infection, VE* _{S}*, in seronegative adult volunteers. In addition, the point estimates for the absolute efficacy of live vaccine were higher for efficacy against symptomatic illness given infection, VE

The challenge studies did not yield particularly useful information for valid estimation of VE* _{I}* for either of these vaccines. Because of the difficulty of directly measuring the probability that an infected individual will infect a susceptible individual, studies such as this one often must use potential surrogate measures of infectiousness. The VE

Overall, the combined efficacy, VE* _{C}*, was consistently higher for the live vaccine when compared with the inactivated vaccine. VE

Although these results provide significant insight into the specific components of vaccine efficacy, more data are needed to assess additional factors key to estimating vaccine efficacy under other conditions. By combining the information from the challenge studies and the phase III community-based vaccine trials and observational studies, we find evidence that the VE* _{SP}* for the live vaccine is consistently higher than that for inactivated vaccine in children, but not necessarily in adults (11, 21). This disparity is probably due to prior immunity in adults, which is not present in very young children. The challenge studies included here were conducted among adults with little or no prior immunity to the challenge strain, which indicates that these results may also be somewhat applicable to children. The effects may be larger in children given that even adults seronegative for specific influenza strains have had greater previous exposure to seasonal influenza than young children have. In the event of an influenza pandemic caused by a novel influenza strain, everyone in the population should be immunologically naïve to the emergent strain. Because the challenge study data used in this analysis challenged only those adult volunteers who were seronegative to the challenge strain, these vaccine efficacy results could be applicable to a pandemic situation, although, again, the effect may be larger given the novelty of the pandemic strain.

The challenge studies all administered homologous strains of influenza during the challenge. We were unable to identify influenza challenge studies that met our selection criteria in which the challenge strain was heterologous to the vaccine strain; therefore, it was not possible to estimate efficacy measures from experimental challenge study data when the vaccine was poorly matched for comparison. There is significant interest in estimating vaccine efficacy for poorly matched strains because the prepandemic vaccines currently being developed will likely be poorly matched to the pandemic strain when a pandemic strain emerges. On the other hand, data from community-based trials in years when poorly matched strains of influenza circulated in the community can provide insight into how well influenza vaccines protect against poorly matched strains.

In the absence of reliable estimates from vaccine trials, the vaccine efficacy values given in Table 3 could be used as rough guides in planning potential vaccination strategies for seasonal influenza in children and pandemic influenza in the community at large. This task could be accomplished by using mathematical models (10), a subject for further research (25).

Author affiliations: Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, Seattle, Washington (Nicole Basta); Program in Biostatistics and Biomathematics, Vaccine and Infectious Disease Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington (Nicole Basta, M. Elizabeth Halloran, Laura Matrajt, Ira M. Longini, Jr.); Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, Washington (M. Elizabeth Halloran, Ira M. Longini, Jr.); and Department of Applied Mathematics, University of Washington, Seattle, Washington (Laura Matrajt).

This work was partially supported by National Institute of General Medical Sciences MIDAS grant U01-GM070749, National Institute of Allergy and Infectious Diseases grant R01-AI32042, and a contract from MedImmune (Gaithersburg, Maryland), which makes a live influenza vaccine.

The authors thank Dr. Yang Yang for statistical help and insights with this paper.

- AR
- attack rate
- CI
- confidence interval
- RR
- relative risk
- VE
- vaccine efficacy
- VE
_{C} - combined vaccine efficacy
- VE
_{I} - vaccine efficacy for infectiousness
- VE
_{P} - vaccine efficacy for illness given infection
- VE
_{S} - vaccine efficacy for susceptibility
- VE
_{SP} - vaccine efficacy for infection-confirmed influenza illness

We compute relative efficacy by comparing the vaccine efficacy estimates for the live, attenuated vaccine with those for the inactivated vaccine. In the case of VE* _{SP}*, we have VE

To derive a simple, tractable expression, we assume that people mix homogeneously. We assume that an infected person will become symptomatic with probability *k*, 0 ≤ *k* ≤ 1*,* that is, pathogenicity. Furthermore, we assume that being infectious and asymptomatic will have a multiplicative effect on infectiousness in the sense that an infectious, asymptomatic person will be relatively *m* times as infectious as a symptomatic person, where 0 ≤ *m* ≤ 1. We parameterize the vaccine efficacies, described in the text, as vaccine efficacy for susceptibility, VE_{S}= 1−θ, vaccine efficacy for infectiousness, VE_{I} = 1−ϕ, and vaccine efficacy for disease symptoms, conditioned on being infected, as VE*P* = 1−ψ. We assume a multiplicative model for the vaccine efficacy for symptoms and infections so that VE*SP* = 1−θψ.

We follow the format from Longini et al. (26) and Hill and Longini (27) to derive functions of the efficacy measures. We define the basic reproductive number for a given infectious disease as the expected number of secondary infections resulting from a single, typical infectious individual in a completely susceptible population. We let *r*_{0} be the basic reproductive number for an unvaccinated, infectious, symptomatic individual. Then, the overall basic reproductive number, *R*_{0}, for the disease is

(1)

(2)

We are interested in computing the expected number of secondary infections produced by a *typical* infected person during his or her entire infectious period, at the beginning of the epidemic. We let *f* be the fraction of the susceptible population that receives vaccine; *I*_{0} and *I*_{1} are the number of secondary unvaccinated and vaccinated cases, respectively. From equation 2 and the law of total probability, we find that

(3)

The expression in the first set of brackets represents the probability of being infected by an unvaccinated, infectious, asymptomatic person (the first summand) plus the probability of being infected by an unvaccinated, symptomatic person (the second summand).

The expression in the second set of brackets represents the probability of being infected by a vaccinated person and again has 2 summands, each representing an asymptomatic and a symptomatic, vaccinated, infectious person. In both summands, the probability of being infected is reduced by a factor of ϕ because of the vaccine efficacy for infectiousness. The first summand represents the probability of being infected by a vaccinated, asymptomatic person. In this instance, the probability that he or she will be asymptomatic is 1−ψk. The last summand represents the probability of being infected by a vaccinated, symptomatic person, so it is reduced by ψ.

Rearranging terms, we have

(4)

Similarly, the number of secondary infections among the vaccinated susceptible population is

(5)

We define the next generation matrix as

(6)

We have given a heuristic derivation of the next-generation matrix, equation 6, but the matrix can also can be derived from local stability analysis around the initial conditions based on the system of differential equations for the system by using a construction similar to that given in Hill and Longini et al. (27); also refer to Farrington (28).

The largest eigenvalue of **M*** _{f}* is the reproductive number with the fraction

(7)

If nobody is vaccinated, that is, *f* = 0, then *R*_{f} = *R*_{0}, in agreement with our previous definition. If *R*_{f} > 1, the epidemic grows, whereas, if *R*_{f} ≤ 1, the epidemic will die out. We define the combined efficacy, VE* _{C}*, by examining the reproductive number when everyone in the population is vaccinated, that is,

(8)

Then, the combined efficacy is

(9)

VE* _{C}* is a useful index because it assesses the combined effect of all 3 vaccine efficacy components, that is, VE

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