Flu Vaccine Effectiveness: Questions and Answers for Health Professionals
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- How do we measure how well influenza vaccines work?
- How do vaccine effectiveness studies differ from vaccine efficacy studies?
- What factors can affect the results of influenza vaccine efficacy and effectiveness studies?
- Can you describe biases that are important to consider for observational studies measuring vaccine effectiveness?
- How well do influenza vaccines work during seasons in which the flu vaccine is not well matched to circulating influenza viruses?
- What is the evidence that influenza vaccines work?
- How well does the live attenuated influenza vaccine (LAIV) work compared to inactivated influenza vaccine (IIV)?
- How does CDC monitor vaccine effectiveness?
Two general types of studies are used to determine how well influenza vaccines work: randomized controlled trials and observational studies. These study designs are described below.
Randomized controlled trials (RCTs)
The first type of study design is called a randomized controlled trial (RCT). In a RCT, volunteers are assigned randomly to receive an influenza vaccine or a placebo (e.g., a shot of saline). Vaccine efficacy is measured by comparing the frequency of influenza illness in the vaccinated and the unvaccinated (placebo) groups. The RCT study design minimizes bias that could lead to invalid study results. Bias is an unintended systematic error in the way researchers select study participants, measure outcomes, or analyze data that can lead to inaccurate results. In a RCT, vaccine allocation is usually double-blinded, which means neither the study volunteers nor the researchers know if a given person has received vaccine or placebo. National regulatory authorities, such as the Food and Drug Administration (FDA) in the United States, require RCTs to be conducted and to demonstrate the protective benefits of a new vaccine before the vaccine is licensed for routine use. However, some vaccines are licensed based on RCTs that use antibody response to the vaccine as measured in the laboratory, rather than decreases in influenza disease among people who were vaccinated.
The second type of study design is an observational study. There are several types of observational studies, including cohort and case-control studies. Observational studies assess how influenza vaccines work by comparing the occurrence of influenza among people who have been vaccinated compared to people not vaccinated. Vaccine effectiveness is the percent reduction in the frequency of influenza illness among vaccinated people compared to people not vaccinated, usually with adjustment for factors (like presence of chronic medical conditions) that are related to both influenza illness and vaccination. (See below for details.)
Vaccine efficacy refers to vaccine protection measured in RCTs usually under optimal conditions where vaccine storage and delivery are monitored and participants are usually healthy. Vaccine effectiveness refers to vaccine protection measured in observational studies that include people with underlying medical conditions who have been administered vaccines by different health care providers under real-world conditions.
Once an influenza vaccine has been licensed by FDA, recommendations are typically made by CDC’s Advisory Committee for Immunization Practices (ACIP) for its routine use. For example, ACIP now recommends annual influenza vaccination for all U.S. residents aged 6 months and older. These universal vaccine recommendations make it unethical to perform placebo-controlled RCTs because assigning people to a placebo group could place them at risk for serious complications from influenza. Also, observational studies often are the only option to measure vaccine effectiveness against more severe, less common influenza outcomes, such as hospitalization.
The measurement of influenza vaccine efficacy and effectiveness can be affected by virus and host factors as well as the study methodology used. Therefore, vaccine efficacy/effectiveness point estimates have varied among published studies.
The protective benefits of influenza vaccination are generally lower during flu seasons where the majority of circulating influenza viruses differ from the influenza viruses used to make the vaccines. Influenza viruses are continuously changing through a natural process known as antigenic drift. (For more information, see How the flu virus can change: Drift and Shift.) However, the degree of antigenic drift and the frequency of drifted viruses in circulation can vary for each of the three or four viruses included in the seasonal flu vaccine. So even when circulating influenza viruses are mildly or moderately drifted in comparison to the vaccine, it is possible that people may still receive some protective benefit from vaccination; and if other circulating influenza viruses are well matched, the vaccine could still provide protective benefits overall.
In addition to virus factors, host factors such as age, underlying medical conditions, history of prior infections and prior vaccinations can affect the benefits received from vaccination.
Study Design Factors
Experts consider RCTs to be the best study design because they are less susceptible to biases. However, as stated above, these studies cannot be conducted when vaccination is recommended in a population and these studies are very difficult to conduct for more severe outcomes that are less common. There are several observational study designs, but many programs currently use the test-negative, case-control design. In the test-negative design, people who seek care for an acute respiratory illness are enrolled at ambulatory care settings (such as outpatient clinics, urgent care clinics, and emergency department) and information is collected about the patients’ influenza vaccination status. All participants are tested for influenza using a highly specific and sensitive test for influenza virus infection, such as reverse transcription polymerase chain reaction (RT-PCR). The ratio of vaccinated to unvaccinated persons (i.e., the odds of influenza vaccination) is then compared for patients with and without laboratory-confirmed influenza. The test-negative design removes selection bias due to health-care seeking behaviors. In addition to the test-negative design, there are additional observational study designs that have been used to estimate vaccine effectiveness.
Factors Related to Measuring Specific versus Non-Specific Outcomes
For both RCTs and observational studies, the specificity of the outcome measured in the study is important. Non-specific outcomes, such as pneumonia hospitalizations or influenza-like illness (ILI) can be associated with influenza virus infections as well as infections with other viruses and bacteria. Vaccine efficacy/effectiveness estimates against non-specific outcomes are generally lower, depending on what proportion of the outcome measured is attributable to influenza. For example, a study among healthy adults found that the inactivated influenza vaccine (i.e., the flu shot) was 86% effective against laboratory-confirmed influenza, but only 10% effective against all respiratory illnesses in the same population and season. Laboratory-confirmed influenza virus infections, by RT-PCR or viral culture, are generally the most specific outcomes for vaccine efficacy/effectiveness studies.
Serologic assays to detect influenza infection (i.e., which require a four-fold rise in antibody titers against influenza viruses detected from paired sera) were often used in past flu VE studies to detect influenza infections prior to more accurate tests, such as RT-PCR, becoming more widely available. The problem with VE studies that use serology to test for influenza infection, is that vaccination elevates antibody levels, similar to infection. New influenza infections could be missed in a vaccinated person since antibodies are already high and a four-fold increase doesn’t develop. Therefore, serologic testing methods can result in biased VE estimates that inflate VE
Can you describe biases that are important to consider for observational studies measuring vaccine effectiveness?
Observational studies are subject to various forms of bias (see above for definition) more so than RCT studies. Therefore, it is important that bias be minimized with the study design or adjusted for in the analysis. Observational studies of influenza vaccine effectiveness can be subject to three forms of bias: confounding, selection bias, and information bias.
Confounding occurs when the effect of vaccination on the risk of the outcome being measured (e.g., influenza-related hospitalizations confirmed by RT-PCR) is distorted by another factor associated both with vaccination (the exposure) and the outcome. In RCTs, confounding factors are expected to be evenly distributed between vaccinated and unvaccinated groups. This is not true of observational studies. For example, chronic medical conditions can confound the association between influenza vaccination and hospitalization with influenza in observational studies. Chronic medical conditions increase the risk of influenza-related hospitalization and vaccination coverage often is higher among people with chronic medical conditions. Therefore, the presence of a chronic medical condition in a study participant is a potential confounding factor that should be considered in analysis. This is an example of confounding by indication because those at greatest risk for the outcome being measured (i.e., influenza associated hospitalization) are targeted for vaccination, and therefore, they are more likely than those without a chronic medical condition to receive a flu vaccine. Not adjusting for confounders could bias the vaccine effectiveness estimate away from the true estimate. In the example given, the vaccine effectiveness estimate could be biased lower, or towards lower effectiveness.
Selection bias occurs when people with the outcome being measured by the study (i.e., influenza infection) differ from people who do not have the outcome. In observational studies of influenza vaccine effectiveness, people with and without influenza may have different likelihoods of being vaccinated, and this can bias the estimate of vaccine effectiveness. For example, people who visit their health care provider in outpatient settings (e.g., clinics and urgent care) are more likely to be vaccinated than people who do not go to a provider for care. If controls are selected from a different population than the cases (e.g., cases are from a clinic and controls from a community sample) with different health care seeking behaviors, selection bias related to health care seeking (and the likelihood to be vaccinated) may be introduced. The test-negative study design minimizes selection bias related to health care seeking by enrolling patients who seek care for a respiratory illness. This study design is used by many studies globally, including CDC-funded networks that measure vaccine effectiveness.
Information bias occurs if exposures or outcome information are based on different sources of information for people with and without the disease of interest. For example, if researchers obtain information on vaccination for children with influenza from immunization records but ask parents of children without influenza if the child was vaccinated, this difference in data collection procedures could bias the results of the study.
How well do influenza vaccines work during seasons in which the flu vaccine is not well matched to circulating influenza viruses?
As described above, when the virus components of the flu vaccine are not well matched with circulating influenza viruses, the benefits of influenza vaccination may be reduced. However, the degree of antigenic drift from vaccine viruses and the proportion of circulating drifted viruses can vary. As a result, even when circulating influenza viruses are mildly or moderately drifted in comparison to the vaccine, it is still possible that people may receive some protective benefit from influenza vaccination. In addition, even when some circulating influenza viruses are significantly drifted, it is possible for other influenza viruses in circulation to be well matched to the vaccine. It is not possible to predict how well the vaccine and circulating strains will be matched in advance of the influenza season, nor is it possible to predict how this match may affect vaccine effectiveness.
Adults 65 years or older
Among older adults, annual influenza vaccination was recommended based on the high burden of influenza-related disease and demonstrated vaccine efficacy among younger adults. One RCT of adults aged 60 years and older relied on serology for confirmation of influenza and reported a vaccine efficacy of 58% (95% confidence interval (CI): 26-77). However, it is unknown if infections were missed by serology among the study participants that were vaccinated (and if the vaccine efficacy estimate is biased upwards – see previous description of how bias can occur in VE studies that test for influenza using serology). A meta-analysis of observational studies that used the test-negative design provided VE estimates for adults aged >60 years against RT-PCR confirmed influenza infection. This meta-analysis reported significant vaccine effectiveness of 52% (95% CI: 41-61) during seasons when the vaccine and circulating viruses were well-matched . During seasons when the circulating viruses were antigenically drifted (not well matched), reported VE was 36% (95% CI: 22-48)3.
An RCT that compared a high-dose, inactivated influenza vaccine (containing four times the standard amount of influenza antigen) to standard dose vaccine in persons aged 65 years or older during the 2011-12 and 2012-13 influenza seasons found that rates of laboratory-confirmed influenza were 24% lower (95% CI: 10-37) among persons who received high-dose vaccine compared to standard dose influenza vaccine, indicating that high-dose vaccine provided 24% better protection against influenza than standard dose vaccine in this trial.
Several observational studies have reported significant vaccine effectiveness against RT-PCR confirmed influenza-related hospitalization among older adults. A three-year study (2006-07 through 2008-09) in Tennessee that used a test-negative design reported vaccine effectiveness of 61% (95% CI: 18-83) among hospitalized adults >50 years of age. In an analysis of two additional seasons, including 2010-11 and 2011-2012 (excluding 2009-10), VE was 58% (95% CI: 8-81) against RT-PCR confirmed influenza associated hospitalizations for persons >50 years of age for the five seasons combined.
Several RCTs have been done in healthy adults aged <65 years[7,8,9,10,11,12]. These studies have reported vaccine efficacy estimates ranging from 16%-75%; VE of 16% was reported during a season with few influenza infections. An RCT in South Africa among HIV infected adults reported vaccine efficacy of 76% (95 CI 9-96). A meta-analysis that included data from RCTs of licensed inactivated influenza vaccines reported a pooled vaccine effectiveness of 59% (95% CI 51-67) against influenza confirmed by RT-PCR or viral culture. In addition, RCTs of cell-based inactivated influenza vaccines (IIVs) and recombinant trivalent HA protein vaccines have been performed among healthy adults. In general, efficacy estimates for these types of vaccines are similar to other inactivated influenza vaccines that are egg-based[15,16,17].
In a four-year RCT of inactivated vaccines among children aged 1–15 years, vaccine efficacy was estimated at 77% against influenza A (H3N2) and 91% against influenza A (H1N1) virus infection. An RCT of children aged 6–24 months reported vaccine efficacy of 66% against laboratory-confirmed influenza in 1999-2000 but no vaccine efficacy during the second year when there was little influenza activity. During 2010-11, the vaccine efficacy of a quadrivalent inactivated vaccine among children aged 3-8 years was 59% (95% CI: 45%-70%). In addition, a cluster-randomized trial conducted in Hutterite communities in Canada found that vaccinating children aged 3 to 15 years with trivalent inactivated influenza vaccine before the 2008-09 season reduced RT-PCR confirmed influenza in the entire community by 61% (95% CI: 8-83), including a 59% reduction (95% CI: 5-82) in confirmed influenza among non-vaccinated community members, evidence of the “indirect” effect of influenza vaccination on prevention on disease transmission.
Several RCTs of live attenuated influenza vaccines among young children have demonstrated vaccine efficacy against laboratory confirmed influenza with estimates ranging from 74%-94%[22,23,24,25]. A study conducted among children aged 12 through 36 months living in Asia during consecutive influenza seasons reported efficacy for live attenuated influenza vaccine of 64%–70%.
An RCT conducted among pregnant women in South Africa during 2011 and 2012 reported vaccine efficacy against RT-PCR confirmed influenza of 50% among HIV-negative women and 58% among HIV-positive women vaccinated during the third trimester. In addition, the trial showed that vaccination reduced the incidence of laboratory-confirmed influenza among infants born to HIV-negative women by 49%; the study was unable to assess vaccine efficacy among infants of HIV infected women. An observational study in the United States during 2010-11 and 2011-12 using a test-negative design reported vaccine effectiveness of 44% (95% CI: 5 to 67) against influenza among pregnant woman.
A randomized trial in Bangladesh found that babies born to mothers vaccinated during pregnancy with trivalent inactivated influenza vaccines were significantly less likely to be born small for gestational age and weighed an average of 200g more than babies born to unvaccinated mothers[29,30]. No effect of maternal immunization on infant birth weight was reported in the South African trial described above. Some observational studies in developed and developing countries have found lower risk of prematurity or low birth weight in babies born to vaccinated mothers, but the effect has not been consistently demonstrated[31,32,33,34,35].
How well does the live attenuated influenza vaccine (LAIV) work compared to inactivated influenza vaccine (IIV)?
Three randomized clinical trials comparing live attenuated influenza vaccine to trivalent inactivated influenza vaccine in young children, 2-8 years of age, suggested that live attenuated influenza vaccine had superior efficacy compared to inactivated influenza vaccine[36,37,38]. Recently, several observational studies suggest that LAIV did not consistently provide better protection against influenza than inactivated vaccine, especially against influenza caused by the 2009 H1N1 pandemic virus[39,40,41]. However, a randomized, school-based study in Canada reported lower rates of confirmed influenza among students vaccinated with live-attenuated vaccine compared to students vaccinated with inactivated influenza vaccine, as well as decreased influenza transmission among family members of students vaccinated with live-attenuated influenza vaccines.
Clinical trials during 2004-05, 2005-06, and 2007-08 that compared inactivated influenza vaccines and live attenuated influenza vaccines to no vaccine among adults suggested that inactivated influenza vaccines provided better protection against influenza than live attenuated influenza vaccines in adults7,8.
CDC monitors vaccine effectiveness annually through the Influenza Vaccine Effectiveness (VE) Network, a collaboration with participating institutions in five geographic locations. These institutions enroll patients with respiratory symptoms at ambulatory clinics and test for influenza by RT-PCR. Vaccine effectiveness is estimated using the test negative design, comparing proportions (odds) of influenza vaccination among patients with and without influenza. Statistical methods are used to account for differences in age, race and underlying medical conditions that might influence vaccine effectiveness. Estimates are reported annually, and often, an early estimate is reported during the season. Since the match between circulating and vaccine viruses is not known before the season, annual estimates of vaccine effectiveness give a real-world look at how well the vaccine protects against influenza caused by circulating viruses each season.
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- Page last updated: January 29, 2016
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