Abstract
Background
Influenza is a major cause of morbidity and mortality worldwide. The Pragmatic Assessment of Influenza Vaccine Effectiveness (VE) in the Department of Defense (PAIVED) study compared the relative VE (rVE) of 2 licensed influenza vaccines (cell culture-based inactivated influenza vaccine [ccIIV] and recombinant influenza vaccine [RIV]) to egg-based inactivated influenza vaccine (eIIV).
Methods
Between 2018 and 2021, PAIVED randomized (1:1:1) eligible Military Health System beneficiaries to receive eIIV, ccIIV, or RIV during an influenza season. Participants received weekly surveys querying influenza-like illness (ILI); those reporting ILI completed an online symptom diary, were interviewed by research staff, and underwent nasal swab collection. Laboratory-confirmed influenza was the primary outcome. Immunogenicity assessment was conducted in a subset of participants.
Results
Among 15 432 participants, influenza was identified in 87/5130 (1.7%) ccIIV recipients, 79/5154 (1.5%) RIV recipients, and 69/5148 (1.3%) eIIV recipients during their season of enrollment. rVE of ccIIV compared to eIIV was −27% (95% confidence interval [CI], −73%, 8%), and rVE of RIV compared to eIIV was −14% (95% CI −58%, 17%). RIV (N = 375) recipients were more likely to seroconvert (4-fold increase) than those who received eIIV (N = 355) (A/H1N1: 49.1% vs 29.0%; A/H3N2: 71.2% vs 30.4%; B/Victoria 24.5% vs 13.8%; B/Yamagata 17.1% vs 5.4%; P < .001 for all). ccIIV (N = 357) recipients were more likely to seroconvert only against A/H3N2 compared with eIIV recipients (44.0% vs 30.4%) (P < .001).
Conclusions
Significant differences in VE were not detected comparing egg-based to non-egg–based influenza vaccines, although immunogenicity differences were observed; low influenza case numbers impacted VE estimate precision.
Trial Registration
Keywords: influenza, influenza-like illness, vaccine, immunogenicity, symptoms
This randomized pragmatic trial of 3 influenza vaccine types did not detect a significant difference in effectiveness in preventing influenza or ILI in predominantly healthy, working-age adults, although anti-hemagglutinin seroconversion was more frequent in participants who received non-egg-based vaccines.
Influenza exerts a significant health burden ranging from self-limited illness, which may impact daily activities, to severe disease and death. In the United States (US), the Centers for Disease Control and Prevention estimated 9–40 million symptomatic influenza infections occurred annually from 2010 to 2023 (excluding 2020–2021) [1]. Influenza vaccination decreases risk of hospitalization and death [2, 3], and has been associated with lower symptom severity [4, 5] and fewer workdays lost [6] due to influenza. US military personnel receive annual influenza vaccination to protect operational readiness [7]. Influenza vaccine effectiveness (VE) varies by season, depending upon the degree of match between circulating influenza strains and those included in the vaccine [8–11].
Influenza vaccines have traditionally been produced using embryonated chicken eggs as the viral growth substrate. Egg-based vaccines have been successful, but disadvantages include reliance on adequate egg supply, comparatively long production timeframes, and development of egg-adaptive mutations that theoretically may affect VE [12]. Newer vaccine manufacturing techniques include producing the antigen in mammalian cells (ie, cell culture-based), and production of recombinant hemagglutinin (HA) protein in insect cells. These newer vaccines avoid egg-adaptive mutations and can be produced more quickly; however, they remain more expensive than egg-based vaccines [13].
Observational, non-randomized studies comparing the relative VE (rVE) of cell culture–based inactivated influenza vaccine (ccIIV) and recombinant influenza vaccine (RIV) to egg-based inactivated influenza vaccine (eIIV) have produced inconsistent results. Several studies observed no difference between ccIIV and eIIV [14–16], one found ccIIV was modestly more effective than eIIV [17], and another determined ccIIV was more effective than eIIV only against influenza B [18].
RIV contains 3 times more HA antigen than eIIV or ccIIV. In several studies, participants age 50+ years who received RIV were 13% to 30% less likely to develop influenza than participants who received eIIV [16, 19, 20]; other studies observed no difference in influenza risk between RIV and eIIV recipients [21].
The Pragmatic Assessment of Influenza Vaccine Effectiveness in the Department of Defense (PAIVED) study was a randomized, open-label comparison of real-world effectiveness of 3 different types of licensed influenza vaccines, conducted over 4 influenza seasons (2018–19, 2019–20, 2020–21, 2021–22) in the setting of required annual influenza vaccination among military populations. The primary goal of PAIVED was to determine whether RIV or ccIIV prevent more laboratory-confirmed influenza cases than eIIV. Additionally, this trial also evaluated rVE against influenza-like illness (ILI) and ILI-associated hospitalization, and compared symptom severity between egg and non-egg–based vaccine recipients who developed an ILI ± influenza. In a subset of participants, PAIVED assessed differences in humoral immune response between egg and non-egg–based influenza vaccines.
METHODS
Ethical Approval
PAIVED was approved by the Infectious Disease Clinical Research Program (IDCRP) Scientific Review Board and the Uniformed Services University Institutional Review Board (IDCRP-120). All study participants provided written informed consent upon enrollment in the study. This study was conducted following good clinical practice and according to the Declaration of Helsinki guidelines. This study is registered at ClinicalTrials.gov (identifier NCT03734237). CONSORT (Consolidated Standards of Reporting Trials) guidelines have been followed.
Study Population
Adults (18+ years) able to receive care in a military treatment facility were eligible for PAIVED, unless they had already received an influenza vaccine that season or could not receive an influenza vaccine. Study enrollment and vaccination were integrated into annual, large scale, high-throughput “vaccination campaigns” at 10 sites in the continental US (Supplementary Appendix); the number of sites varied by influenza season. Participants were enrolled for a single season and could enroll in subsequent seasons. A pre-specified number of participants at 7 sites were offered the opportunity to also participate in an immunogenicity substudy.
Randomization
Participants were randomly allocated to receive influenza vaccines in a 1:1:1 ratio: the control group received eIIV (Fluarix® Quadrivalent [GlaxoSmithKline] [15 µg HA/strain], FluLaval® Quadrivalent [ID Biomedical Corp. of Quebec] [15 µg HA/strain], or Afluria® Quadrivalent [Seqirus] [15 µg HA/strain], according to site availability); the test groups received RIV (FluBlok® Quadrivalent [Protein Sciences] [45µg HA/strain]), or ccIIV (Flucelvax™ Quadrivalent [Seqirus] [15 µg HA/strain]). The randomization procedures are described in Supplementary Appendix. Participants and staff administering the vaccine were aware of study group assignment; influenza vaccine record requirements in military participants necessitated the open-label design.
Follow-Up
Beginning 2 weeks after vaccination, most participants received weekly text or email surveys inquiring about ILI symptoms during the past 7 days (an alternate strategy was used at one site to accommodate military-unique circumstances; Supplementary Appendix). Enrollment began on 7 November 2018 in season 1 and the last ILI survey was sent on 17 May 2019. Subsequent seasons enrolled and surveilled subjects from 10 October 2019 to 30 April 2020, 5 October 2020 to 26 April 2021, and 17 September 2021 to 26 May 2022.
In season 1, a participant was considered to have an ILI if they reported having at least 1 symptom from all 3 of the following categories: (1) fever or feeling feverish or chills/night sweats, (2) cough or sore throat, and (3) muscle/body aches or fatigue (tiredness). During subsequent seasons, the ILI definition was broadened to increase sensitivity for detecting influenza; a subject was considered to have an ILI if they reported cough or sore throat and at least one of the following: (1) feverish/chills, and/or (2) muscle/body aches or fatigue.
Participants who met the ILI study definition underwent an acute visit as soon as possible after reporting the ILI, and a convalescent visit approximately 21 days later. These visits were conducted in person or virtually, depending on the season. A nasal swab and blood sample (serum or self-administered fingerstick capillary blood) were collected during the acute ILI visit, and an additional blood sample was collected at the convalescent visit; illness duration was assessed during both visits. The Supplementary Appendix contains additional details about sample and data collection processes.
Participants with ILI were asked to complete the inFLUenza Patient Reported Outcome (FLU-PRO) instrument daily for 7 days after reporting an ILI. The FLU-PRO instrument is a well-validated patient-reported outcomes survey that includes symptoms in 6 domains (respiratory, systemic, gastrointestinal, nose, throat, and eyes) with Likert scale responses ranging from “not at all” to “very much” [22]. Questions on altered sense of smell and taste were added in 2020 (FLU-PRO Plus) [23, 24].
Study Outcomes
PAIVED's primary outcome was laboratory-confirmed influenza, detected by reverse transcription polymerase chain reaction (RT-PCR) from a study-collected swab and/or a positive clinical influenza test, at least 14 days postvaccination. Study-collected swabs underwent multiplex RT-PCR (NxTAG Respiratory Pathogen Panel, Diasorin/Luminex, Austin, TX) for the detection of influenza (A, A/H1, A/H3, and B) as well as 16 other respiratory pathogens [25]. Influenza strains were typed and amplicon sequencing was performed utilizing a universal primer sequencing approach for influenza A and B viruses [26, 27]. Analysis of amplicon sequencing data was performed using a standard deployment of the Iterative Refinement Meta Assembler [28]. Clinical influenza test results were obtained through abstraction of electronic medical record (EMR) data from the Military Health System (MHS) Data Repository (MDR), which records all healthcare-related encounters, tests, and procedures received through the MHS. All influenza, as well as influenza by type and subtype, were compared across vaccine groups (RIV vs eIIV; ccIIV vs eIIV).
The secondary aim of the study was to compare immunogenicity of the 2 non-egg–based vaccines to eIIV. Enrolled participants who volunteered for the immunogenicity sub-study provided venous blood specimens before vaccination and 21–35 days postvaccination. Substudy participant characteristics and how they compared to the overall study cohort, along with detailed methods for producing the HA-inhibition and microneutralization titers, are in the Supplementary Material (see Supplementary Appendix and Table S1).
Exploratory outcomes included ILI incidence and ILI-associated hospitalizations, identified via study surveillance and from the MDR using prespecified ICD-10 codes plus COVID-19 related codes (Supplementary Table S2). In addition, ILI duration and severity were compared, and subgroup analyses were performed by season and age group. As this was an rVE study of licensed influenza vaccines in routine use, safety outcomes were not systematically assessed.
Sample Size Estimation
The sample size estimates for this study were based on a power of 0.80, a 1-sided alpha of 0.025, and an assumed influenza incidence of 5% in the eIIV group, to detect at least a 30% difference between egg-based and non-egg–based vaccines. PAIVED was initially designed as a 2-year superiority study with a target enrollment of 10 650; however, a prespecified interim analysis conducted after the first season observed a low influenza incidence, leading to broadening of the ILI definition, extension of the study duration to 3 years, and expansion of the enrollment target to 15 000 participants, with the approval of the Data Monitoring Committee. PAIVED was extended an additional year after worldwide influenza attack rates markedly decreased in 2020–2021.
Statistical Analyses
Influenza, ILI, and seroconversion outcomes were compared between groups using treatment differences and risk ratios. The analysis population included all study-eligible participants who were enrolled, randomized, and vaccinated; individuals who participated in multiple seasons were analyzed as unique participants per season. Participants missing outcome information (either due to lack of response to surveys or missing nasal swabs) were assumed to not have the outcome of interest. rVE was estimated using the risk ratio (1—risk ratio, expressed as a percentage); all rVE estimates used the eIIV recipients as the comparison group (Supplementary Appendix). Illness characteristics were compared using t-tests. Randomized assignment of vaccines was expected to produce comparison groups that were similar with respect to potential confounders. Analyses were performed in R (R Version 4.4.0, R Core Team 2021) using the epiR package (Version 2.0.74). Due to the small number of planned comparisons, no adjustments for multiple comparisons were performed. Sub-group analyses were performed based on surveillance response, season, and age group.
RESULTS
Study Enrollment
Across the 4 years of PAIVED (2018–19, 2019–20, 2020–21, and 2021–22), 15 448 participants were enrolled, randomized, and vaccinated (Figure 1). Ten subjects received a vaccine different from their randomization assignment (randomized to eIIV received ccIIV (n = 1); randomized to ccIIV received eIIV (n = 5) or RIV (n = 1); randomized to RIV received eIIV (n = 2) or ccIIV (n = 1)); as this was a pragmatic study, subjects were analyzed according to the vaccine received. Additionally, 16 participants who were enrolled and vaccinated were excluded from the analysis population due to protocol violations relating to study eligibility criteria (eg, receipt of second influenza vaccine) which prevented meaningful assessment of outcomes. Most vaccines were administered in October (41%) and November (36%) of the study season. Participants were mostly male (68%), active duty (70%), and <45 years of age (79%), with similar demographic characteristics across the 3 vaccine groups (Table 1). Ninety-three percent (14 334/15 432) of participants were unique individuals, most of whom (13 361/14,334, 93%), enrolled in a single season; 857 (6%) participated in 2 seasons, 107 (0.7%) participated in 3 seasons, and 9 (0.06%) participated in all seasons.
Figure 1.
Flowchart of individuals assessed, randomized, vaccinated, and included in the analysis of a pragmatic study comparing the relative effectiveness of licensed cell culture-based inactivated influenza vaccine (ccIIV) and recombinant influenza vaccine (RIV) to egg-based inactivated influenza vaccine (eIIV) in adult military health beneficiaries across 4 influenza seasons (2018–19, 2019–20, 2020–21, 2021–22). aOther reasons for exclusion: withdrew prior to randomization (n = 80); withdrew after randomization, prior to vaccination (n = 5); withdrew HIPAA authorization after vaccination/before surveillance (n = 2). The analysis population includes randomized and vaccinated participants according to the vaccine they received, excluding 16 individuals who were belatedly determined to not meet study eligibility criteria.
Table 1.
Characteristics of Participants Enrolled in PAIVED
| eIIV (N = 5148) | ccIIV (N = 5130) | RIV (N = 5154) | Total (N = 15 432) | |
|---|---|---|---|---|
| Season | ||||
| 2018/19 | 541 (10.5%) | 541 (10.5%) | 540 (10.5%) | 1622 (10.5%) |
| 2019/20 | 1970 (38.3%) | 1949 (38.0%) | 1962 (38.1%) | 5881 (38.1%) |
| 2020/21 | 1080 (21.0%) | 1079 (21.0%) | 1087 (21.1%) | 3246 (21.0%) |
| 2021/22 | 1557 (30.2%) | 1561 (30.4%) | 1565 (30.4%) | 4683 (30.3%) |
| Female | 1639 (31.8%) | 1644 (32.0%) | 1673 (32.5%) | 4956 (32.1%) |
| Education level | ||||
| High school or less | 2261 (44.0%) | 2171 (42.4%) | 2194 (42.7%) | 6626 (43.0%) |
| Associate's degree | 737 (14.3%) | 720 (14.1%) | 758 (14.7%) | 2215 (14.4%) |
| Bachelor's degree | 907 (17.6%) | 982 (19.2%) | 900 (17.5%) | 2789 (18.1%) |
| Higher degree (PhD, Masters) | 1234 (24.0%) | 1249 (24.4%) | 1290 (25.1%) | 3773 (24.5%) |
| Missing | 9 | 8 | 12 | 29 |
| Military status | ||||
| Active duty | 3611 (70.2%) | 3568 (69.6%) | 3602 (69.9%) | 10 781 (69.9%) |
| Retired military | 563 (10.9%) | 594 (11.6%) | 592 (11.5%) | 1749 (11.3%) |
| Dependent | 524 (10.2%) | 521 (10.2%) | 509 (9.9%) | 1554 (10.1%) |
| Recruit | 447 (8.7%) | 445 (8.7%) | 450 (8.7%) | 1342 (8.7%) |
| Missing | 3 | 2 | 1 | 6 |
| Race/Ethnicity | ||||
| White | 2936 (57.0%) | 2937 (57.3%) | 2897 (56.2%) | 8770 (56.8%) |
| Hispanic or Latino | 997 (19.4%) | 951 (18.5%) | 987 (19.2%) | 2934 (19.0%) |
| Black | 563 (10.9%) | 552 (10.8%) | 594 (11.5%) | 1709 (11.1%) |
| Other/unknown | 371 (7.2%) | 382 (7.4%) | 373 (7.2%) | 1127 (7.3%) |
| Asian | 281 (5.5%) | 308 (6.0%) | 303 (5.9%) | 892 (5.8%) |
| Age group, y | ||||
| 18–<25 | 1787 (34.7%) | 1777 (34.6%) | 1789 (34.7%) | 5353 (34.7%) |
| 25–<45 | 2287 (44.4%) | 2310 (45.0%) | 2296 (44.5%) | 6893 (44.7%) |
| 45+ | 1074 (20.9%) | 1043 (20.3%) | 1069 (20.7%) | 3186 (20.6%) |
| Percent response to surveillance—Median (Q1, Q3) | 69.0 (5.6, 100.0) | 72.0 (5.9, 100.0) | 71.4 (5.3, 100.0) | 70.8 (5.6, 100.0) |
Abbreviations: eIIV, egg-based inactivated influenza vaccine; ccIIV, cell culture-based inactivated influenza vaccine; RIV, recombinant influenza vaccine.
Primary Analysis
At least 1 ILI was identified in 5431/15 432 (35%) participants, among whom 235 were laboratory-test positive for influenza (Table 2). One participant with influenza had 2 positive influenza tests (1 influenza B, 1 influenza A/H1N1) separated by 31 days; their first influenza illness (influenza B) was retained for analyses. The majority (164/236, 69.5%) of laboratory-confirmed influenza illness was due to influenza A (57 A/H1N1, 29 A/H3N2, and 78 untyped influenza A), while influenza B accounted for 68 cases (28.9%). Two (0.8%) influenza cases were not subtyped, and 2 clinical tests were positive for both influenza A and B in the MDR. Almost half the influenza cases (49% (115/236)) were solely identified through ILI surveillance/testing, 90 (38.1%) were identified only from positive test results in the MDR, and 31 (13%) were influenza test-positive via both PAIVED surveillance and the MDR. Influenza results were available for 80% of ILIs identified through study surveillance, but only 24% of MDR-identified ILIs (Supplementary Appendix). No influenza hospitalizations occurred.
Table 2.
Influenza Incidence, Risk Ratio, and Relative Vaccine Effectiveness Comparing Cell Culture-Based and Recombinant Influenza Vaccine Recipients to Egg-Based Inactivated Influenza Vaccine Recipients in PAIVED Overall.
| Name | eIIV (N = 5148) | ccIIV (N = 5130) | RIV (N = 5154) | ccIIV Compared With eIIV | RIV Compared With eIIV | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Treatment Difference % (95% CI) | Risk Ratio (95% CI) | Relative Vaccine Effectiveness, % (95% CI) | P Value | Treatment Difference % (95% CI) | Risk Ratio (95% CI) | Relative Vaccine Effectiveness, % (95% CI) | P value | ||||
| Influenzaa | 69 (1.3%) | 87 (1.7%) | 79 (1.5%) | 0.36 (−0.12, 0.83) | 1.27 (0.92, 1.73) | −26.53 (−73.13, 7.53) | 0.14 | 0.19 (−0.27, 0.65) | 1.14 (0.83, 1.58) | −14.36 (−57.59, 17.01) | 0.41 |
| Exploratory outcomes | |||||||||||
| Influenza A | 49 (1.0%) | 58 (1.1%) | 57 (1.1%) | 0.18 (−0.21, 0.57) | 1.19 (0.81, 1.73) | −18.78 (−73.40, 18.63) | 0.37 | 0.15 (−0.24, 0.54) | 1.16 (0.79, 1.70) | −16.19 (−69.88, 20.53) | 0.44 |
| A/H1N1 | 18 (0.3%) | 15 (0.3%) | 24 (0.5%) | −0.06 (−0.28, 0.16) | 0.84 (0.42, 1.66) | 16.37 (−65.75, 57.81) | 0.61 | 0.12 (−0.13, 0.36) | 1.33 (0.72, 2.45) | −33.18 (−145.08, 27.63) | 0.36 |
| A/H3N2 | 9 (0.2%) | 9 (0.2%) | 11 (0.2%) | 0.00 (−0.16, 0.16) | 1.00 (0.40, 2.53) | −0.35 (−152.60, 60.13) | 0.99 | 0.04 (−0.13, 0.21) | 1.22 (0.51, 2.94) | −22.08 (−194.35, 49.37) | 0.66 |
| A/unknown | 22 (0.4%) | 34 (0.7%) | 22 (0.4%) | 0.24 (−0.05, 0.52) | 1.55 (0.91, 2.65) | −55.09 (−164.77, 9.16) | 0.10 | −0.00 (−0.25, 0.25) | 1.00 (0.55, 1.80) | 0.12 (−80.13, 44.61) | 0.99 |
| Influenza B | 20 (0.4%) | 26 (0.5%) | 22 (0.4%) | 0.12 (−0.14, 0.38) | 1.30 (0.73, 2.33) | −30.46 (−133.39, 27.08) | 0.37 | 0.04 (−0.21, 0.28) | 1.10 (0.60, 2.01) | −9.87 (−101.06, 39.96) | 0.76 |
| ILIb | 1821 (35.4%) | 1794 (35.0%) | 1816 (35.2%) | −0.40 (−2.25, 1.44) | 0.99 (0.94, 1.04) | 1.14 (−4.19, 6.19) | 0.67 | −0.14 (−1.98, 1.71) | 1.00 (0.95, 1.05) | 0.39 (−4.96, 5.46) | 0.88 |
| Hospitalized with ILIc | 12 (0.2%) | 16 (0.3%) | 14 (0.3%) | 0.08 (−0.12, 0.28) | 1.34 (0.63, 2.83) | −33.80 (−182.54, 36.64) | 0.44 | 0.04 (−0.16, 0.23) | 1.17 (0.54, 2.52) | −16.53 (−151.70, 46.05) | 0.70 |
Three participants (all in the cell culture-based vaccine group) with untyped influenza were not included in the subtype specific analyses.
aInfluenza was identified using PCR-positive research swabs or positive clinical test results found in the participants' medical records.
bInfluenza-like illness (ILI), identified through surveillance and the Military Health Systems Data Repository.
cHospitalizations with ILI were identified if they had an ILI diagnosis while hospitalized.
Abbreviations: eIIV, egg-based inactivated influenza vaccine; ccIIV, cell culture-based inactivated influenza vaccine; RIV, recombinant influenza vaccine.
The influenza attack rate during participants' season of enrollment was 1.3% (69/5148) in the eIIV group, 1.7% (87/5130) in the ccIIV group, and 1.5% (79/5154) in the RIV group (Table 2). The rVE of ccIIV compared to eIIV against laboratory-confirmed influenza was −26.53% (95% CI −73.13%, 7.53%). Similarly, the rVE of RIV as compared to eIIV was −14.36% (95% CI −57.59%, 17.01%). These rVE estimates were essentially unchanged when all 15 448 randomized and vaccinated participants were analyzed (Supplementary Table S3).
Subgroup and Exploratory Analyses
No statistically significant differences in rVE were found comparing eIIV to either ccIIV or RIV against different subtypes of influenza, against ILI, and against ILI-associated hospitalization (Table 2). Sensitivity analyses that included participants with MDR data who (1) responded to ≥50% of surveillance prompts or (2) responded to any surveillance prompts, yielded similar results (Supplementary Table S4). In addition, no statistically significant differences in rVE were observed when sub-analyses were performed by season and by age group (Supplementary Tables S5 and S6).
Influenza episodes lasted on average, 11.4 days, with 6.0 days of reported limited activity and 2.2 days of reported missed work (Table 3). Average ILI duration was almost 1 day longer for participants who received RIV compared to those who received eIIV (0.94, 95% CI 0.27, 1.60); other influenza and ILI characteristics were similar by vaccine group. FLU-PRO survey responses were available for 167 influenza cases; the maximum total and domain-specific FLU-PRO scores were similar among vaccine types (Supplementary Table S7).
Table 3.
Exploratory Influenza a and Influenza-like Illness (ILI)b Outcomes in PAIVED Participants by Vaccine Group
| eIIV (N = 5148) | ccIIV (N = 5130) | RIV (N = 5154) | ccIIV Versus eIIV (95% CI) | RIV Versus eIIV (95% CI) | |
|---|---|---|---|---|---|
| Influenza | |||||
| Episode duration, mean (SD) | 10.6 (9.0) | 11.6 (7.8) | 11.9 (7.1) | 1.03 (−2.04, 4.11) | 1.36 (−1.56, 4.27) |
| N | 53 | 65 | 66 | … | … |
| Days with limited activity, mean (SD) | 5.8 (5.8) | 6.1 (5.9) | 6.0 (5.5) | 0.31 (−1.87, 2.49) | 0.20 (−1.89, 2.29) |
| N | 50 | 63 | 66 | … | … |
| Days missed work, mean (SD) | 2.0 (2.0) | 2.5 (2.6) | 2.0 (2.4) | 0.54 (−0.36, 1.44) | 0.09 (−0.76, 0.94) |
| N | 48 | 58 | 62 | … | … |
| Total FLU-PRO score, mean (SD) | 1.6 (0.6) | 1.7 (0.7) | 1.5 (0.7) | 0.09 (−0.18, 0.35) | 0.09 (−0.36, 0.18) |
| N | 47 | 60 | 60 | … | … |
| Influenza-like illness | |||||
| Episode duration, mean (SD) | 11.5 (7.1) | 12.0 (8.7) | 12.4 (8.5) | 0.48 (−0.20, 1.16) | 0.94 (0.27, 1.60) |
| N | 1036 | 1055 | 1091 | … | … |
| Days with limited activity, mean (SD) | 5.3 (5.2) | 5.5 (5.9) | 5.6 (6.2) | 0.11 (−0.38, 0.60) | 0.28 (−0.21, 0.78) |
| N | 1000 | 1018 | 1067 | … | … |
| Days missed work, mean (SD) | 2.0 (3.2) | 2.0 (3.3) | 2.1 (3.4) | −0.02 (−0.31, 0.27) | 0.03 (−0.27, 0.32) |
| N | 940 | 964 | 1012 | … | … |
| Total FLU-PRO score, mean (SD) | 1.2 (0.7) | 1.2 (0.7) | 1.2 (0.7) | 0.04 (−0.02, 0.10) | 0.03 (−0.03, 0.09) |
| N | 956 | 971 | 1035 | … | … |
For individuals with more than 1 influenza infection or ILI, the first episode was retained.
The outcomes were collected during scheduled study visits (episode duration, days with limited activity, days of missed work) and using an online survey (FLU-PRO); not all participants had information on all outcomes. Difference between groups was calculated using t-tests.
aInfluenza was identified using PCR-positive research swabs or positive clinical tests found in the participants' medical records.
bInfluenza-like illness (ILI) was identified during weekly surveillance in which participants who were not recruits received weekly surveys inquiring about ILI symptoms. If they met study criteria for ILI, they provided symptom data as well as nasal swabs.
Abbreviations: eIIV, egg-based inactivated influenza vaccine; ccIIV, cell culture-based inactivated influenza vaccine; RIV, recombinant influenza vaccine.
Immunogenicity
Sub-study participants who received RIV (N = 375) were more likely than those who received eIIV (N = 355) to seroconvert (4-fold increase in antibody titers from pre- to post-vaccine) against the vaccine-matched HA component for all 4 influenza subtypes, whereas participants who received ccIIV (N = 357) were more likely than those who received eIIV to seroconvert only against the A/H3N2 vaccine-matched HA component (Table 4). Demographic characteristics were generally similar across vaccine groups in the sub-study; however, there were more women in the eIIV group than in the other 2 groups (Supplementary Table S8).
Table 4.
Relative Risk of Hemagglutination Inhibition Assay Seroconversion (4-fold Increase) From Pre-vaccination to 21–35 Days Post-vaccination, All Seasons (2018/19–2021/22), by Influenza Subtype in PAIVED (N = 1087)
| Seroconversion | eIIV (N = 355) | ccIIV (N = 357) | RIV (N = 375) | ccIIV Compared To EIIV | RIV Compared To eIIV | ||
|---|---|---|---|---|---|---|---|
| Relative Risk of Seroconversion (95% CI) | P value | Relative Risk of Seroconversion (95% CI) | P value | ||||
| A/H1N1 | 103 (29.0%) | 114 (31.9%) | 184 (49.1%) | 1.10 (0.88, 1.37) | 0.42 | 1.69 (1.39, 2.05) | <0.001 |
| A/H3N2 | 108 (30.4%) | 157 (44.0%) | 267 (71.2%) | 1.45 (1.19, 1.76) | <0.001 | 2.34 (1.97, 2.77) | <0.001 |
| B/Victoria | 49 (13.8%) | 48 (13.4%) | 92 (24.5%) | 0.97 (0.67, 1.41) | 0.91 | 1.78 (1.30, 2.43) | <0.001 |
| B/Yamagata | 19 (5.4%) | 24 (6.7%) | 64 (17.1%) | 1.26 (0.70, 2.25) | 0.53 | 3.19 (1.95, 5.21) | <0.001 |
Abbreviations: eIIV, egg-based inactivated influenza vaccine; ccIIV, cell culture-based inactivated influenza vaccine; RIV, recombinant influenza vaccine.
DISCUSSION
In this prospective, pragmatic comparative effectiveness study involving predominantly healthy, working-age US adults, we did not detect statistically significant differences in rates of influenza or ILI when comparing those who received ccIIV or RIV to those who received eIIV, although the CIs were wide. The lack of association between vaccine type received and the outcomes of laboratory-confirmed symptomatic influenza and of ILI was consistent when stratified by influenza season and age, as well as when considering influenza subtype among those with laboratory-confirmed influenza.
Prior studies have reported mixed results regarding rVE of influenza vaccine types. Several studies that demonstrated lower rates of influenza among those who received RIV compared to eIIV [16, 19, 20] were performed in older (50+) populations; other studies did not identify a difference in influenza rates [21, 29]. Comparisons between ccIIV and eIIV have also primarily been in older age groups; 2 studies found no difference in VE between the 2 vaccine types [15, 16], 1 study found ccIIV provided slightly better protection than eIIV against influenza-related hospitalizations [17], and 1 study found that ccIIV was more effective only for influenza B infection [18].
There are some key differences between PAIVED and prior studies; the PAIVED population was generally younger and healthier, and PAIVED utilized both active and passive surveillance to identify influenza and ILI rather than solely focusing on medical encounters and severe outcomes (which are comparatively rare in military populations). Most PAIVED participants had prior influenza vaccination exposure given influenza vaccine requirements for active-duty personnel, and eIIV has historically been the most common formulation administered within the Department of Defense. PAIVED also assessed immunogenicity, which few other comparative rVE studies have done. Similar to the RAIVEN study [28], RIV (and ccIIV in PAIVED) elicited more robust humoral immune response to HA than eIIV vaccine; in PAIVED this translated to higher rates of vaccine-induced anti-HA seroconversion among recipients of RIV than among recipients of eIIV across all 4 influenza subtypes, whereas differences in seroconversion rates were not observed in RAIVEN. The preponderance of A/H1N1 and B virus infections in PAIVED might explain, in part, the lack of apparent protective benefit observed despite the comparatively greater immunogenicity of RIV and ccIIV for the A/H3N2 antigenic components relative to the A/H1N1 and B components. However, the discordance between vaccine immunogenicity and symptomatic influenza risk observed in PAIVED demonstrates a potential pitfall of influenza vaccination strategies relying solely on serologic measurement of antigenic response and highlights the need for additional research into correlates of protection and risk.
Our study has several strengths. The prospective, randomized design of this pragmatic trial resulted in balanced allocation of vaccine types to a study population representative of working-age adults. The outcome of influenza illness was ascertained via active surveillance as well as abstraction of clinical laboratory data from the single-payer EMR. Participants were enrolled and followed at multiple, geographically distributed sites throughout the US, and the study was active in multiple influenza seasons, affording the opportunity for observation across secular trends in influenza risk.
There are key limitations with this study, principally the low number of influenza infection events; this may reflect the effectiveness of all 3 types of vaccines in healthy young and middle-aged adults. COVID-19 pandemic-associated behaviors likely also contributed to lower influenza infection risk. MDR observation enabled substantial capture of additional influenza cases, but this signal was dependent upon participants seeking healthcare and undergoing influenza testing. Ultimately the study did not observe differences in rVE in this population with a low infection rate, despite the study having been extended an additional 2 years beyond the originally planned duration. Given the large CIs around the rVE point estimates, it is possible that a clinically significant rVE may have been found in the setting of higher influenza infection rates. An inherent limitation is that open label studies may be subject to bias; however, the overall ILI incidence did not differ between vaccine groups, suggesting this was likely not a factor. Given the relatively young and healthy population enrolled in PAIVED, we were unable to ascertain whether rVE benefits may be confined to certain vaccine types in specific populations such as older adults or those with concomitant diseases.
CONCLUSION
This study did not detect significant differential effectiveness of newer non-egg–based influenza vaccines compared to egg-based products in preventing influenza illness in a population of predominantly healthy, working-age adults with low influenza incidence.
Supplementary Material
Notes
Author contributions. S. A. R., K. Sc., and E. Z. conducted the analyses. R. E. C., S. A. R., and T. H. B. wrote the initial draft of the manuscript. All authors reviewed the manuscript and provided edits.
Acknowledgments. The authors thank the PAIVED study participants and the team of clinical research coordinators, clinical site managers, data managers and administrative support personnel whose contributions were instrumental to the execution of this project.
Paived Study Group. Brooke Army Medical Center, Fort Sam Houston, TX: B. Clakley; A. E. Markelz; K. Mende; S. Merritt; T. Merritt; A. McClung; C. Murillo; D. Nall
Carl R. Darnall Army Medical Center, Fort Hood, TX: S. Bazan
Lackland Air Force Base, Joint Base San Antonio, TX: A. Swarthout-Ebarb
Henry M. Jackson Foundation, Inc. Bethesda, MD: D. Becher; K. Blankenship; R. Bowers; C. Coles; M. Fritschlanski; M. Grance; N. Moreno; C. Olsen; O. Ortega; H. Park; E. Parmelee; S. Pollett; S. Richard; J. Rothenberg; K. Schmidt
Madigan Army Medical Center, Joint Base Lewis-McChord, WA: C. Baker; J. Bowman; A. Cochran; S. Chambers; R. Colombo; B. Jones; M. Heilweil; A. King; M. Martin; C. Schofield
Naval Medical Center Portsmouth, Portsmouth, VA: S. Banks; J. McNiff; R. Smith; M. Romero; J. Dunham; T. Lalani; R. Tant; M. Cerroni; A. Austin; T. Warkentien
Naval Medical Center San Diego, San Diego, CA: C. Berjohn; K. Gyben; N. Kirkland; D. Larson; R. C. Maves; G. Utz; G. Zollicoffer
Naval Health Clinic Annapolis, Annapolis, MD: C. Friend; J. Modi; A. Saperstein; D. Tilley; M. Wayman
Uniformed Services University of the Health Sciences, Bethesda, MD: T. Burgess; L. Kosh; R. J. O’Connell; M. Simons; A. Williams
United States Air Force School of Aerospace Medicine, Dayton, OH: A. Fries; V. Hogan; S. Purves
Walter Reed National Military Medical Center, Bethesda, MD: I. Barahona; A. Ganesan; J. Jefferson; L. Lawson; S. Loo; S. Loy; M. Macisaac; W. Campbell
Defense Health Agency, Immunization Healthcare Division: L. Collins; L. Housel; S. Seshadri; C. Spooner; D. Hrncir; B. McClenathan; J. Brunader; C. Lohsl; E. Hall; L. Gazlay; C. Marrier; S. Bedno; A. Farmer; A. Nivens; G. Collins; M. Hussain; D. Turner; M. Richardson; J. Ritschl; A. Smith; D. Crosby; J. Simon; A. Williams; K. Matthews; C. Armstrong; D. Parish
Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, United States: J. Powers III
Disclaimer. The views expressed are those of the authors and do not reflect the official policy of the Department of the Army, Department of the Navy, the Department of the Air Force, the Department of Defense, the US Government, US Army Medical Department, US Army Office of the SG, BAMC, MAMC, WRNMMC, WAMC, USUHS, DHA, and the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF). Mention of trade names, commercial products, or organizations does not imply endorsement by the US Government. Investigators followed human subjects' protection 45CFR46 policies.
Financial support. This work (IDCRP-120) was conducted by the Infectious Disease Clinical Research Program (IDCRP), a Department of Defense (DoD) program executed by the Uniformed Services University of the Health Sciences (USU) through a cooperative agreement with The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF). This project has been supported with federal funds from the Defense Health Program (US Department of Defense) and Defense Health Agency Immunization Healthcare Division, under award HU0001190002, the Defense Health Agency Armed Forces Health Surveillance Division Global Emerging Infections Surveillance Branch (ProMIS ID P0014_18_US and P0007_19_US), and from the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), under Inter-Agency Agreement Y1-Al-5072. This project has also been funded in part with federal funds from the National Institutes of Health Clinical Center and the National Cancer Institute, National Institutes of Health, under Contract No. 75N91019D00024 Task Order No. 75N91019F00130. The funding partners were not involved in the study analysis or interpretation of the results.
Contributor Information
Rhonda E Colombo, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA; Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; Department of Medicine, Madigan Army Medical Center, Tacoma, Washington, USA.
Stephanie A Richard, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
Kat Schmidt, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
Christina Schofield, Department of Medicine, Madigan Army Medical Center, Tacoma, Washington, USA.
Anuradha Ganesan, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA; Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.
Wesley Campbell, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA.
David Hrncir, Carl R. Darnall Army Medical Center, Fort Cavazos, Texas, USA; Wilford Hall Ambulatory Surgical Center, Lackland Air Force Base, San Antonio, Texas, USA; Immunization Healthcare Division, Defense Health Agency, Falls Church, Virginia, USA.
Tahaniyat Lalani, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA; Department of Medicine, Naval Medical Center Portsmouth, Portsmouth, Virginia, USA.
Katrin Mende, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA; Department of Medicine, Brooke Army Medical Center, San Antonio, Texas, USA.
Ana E Markelz, Department of Medicine, Brooke Army Medical Center, San Antonio, Texas, USA.
Catherine M Berjohn, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; Department of Medicine, Naval Medical Center San Diego, San Diego, California, USA.
Laurie Housel, Immunization Healthcare Division, Defense Health Agency, Falls Church, Virginia, USA; Womack Army Medical Center, Fort Bragg, North Carolina, USA.
Dorothy Becher, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
Elizabeth R Zell, Stat-Epi Associates, Inc, Ponte Vedra Beach, Florida, USA.
Daniel Ewing, Viral Diseases Program, Naval Medical Research Command, Silver Spring, Maryland, USA.
Appavu K Sundaram, Viral Diseases Program, Naval Medical Research Command, Silver Spring, Maryland, USA.
Jitendrakumar R Modi, Navy Medicine Readiness and Training Command, Annapolis, Maryland, USA.
Adam Saperstein, Department of Family Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
Drake H Tilley, Jr., Navy Medicine Readiness and Training Command, Annapolis, Maryland, USA
Alan Williams, Department of Family Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
Bruce McClenathan, Immunization Healthcare Division, Defense Health Agency, Falls Church, Virginia, USA; Womack Army Medical Center, Fort Bragg, North Carolina, USA.
Limone Collins, Immunization Healthcare Division, Defense Health Agency, Falls Church, Virginia, USA.
Christina Spooner, Immunization Healthcare Division, Defense Health Agency, Falls Church, Virginia, USA.
Srihari Seshadri, Immunization Healthcare Division, Defense Health Agency, Falls Church, Virginia, USA.
Anthony Fries, Public Health and Preventive Medicine Department, US Air Force School of Aerospace Medicine, Wright-Patterson AFB, Ohio, USA.
Ryan C Maves, Department of Medicine, Naval Medical Center San Diego, San Diego, California, USA; Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
John H Powers III, Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA.
Robert J O’Connell, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
Simon D Pollett, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
Mark P Simons, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
Christian L Coles, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, USA.
Timothy H Burgess, Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA; Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
the PAIVED Study Group:
B Clakley, A E Markelz, K Mende, S Merritt, T Merritt, A McClung, C Murillo, D Nall, S Bazan, A Swarthout-Ebarb, D Becher, K Blankenship, R Bowers, C Coles, M Fritschlanski, M Grance, N Moreno, C Olsen, O Ortega, H Park, E Parmelee, S Pollett, S Richard, J Rothenberg, K Schmidt, C Baker, J Bowman, A Cochran, S Chambers, R Colombo, B Jones, M Heilweil, A King, M Martin, C Schofield, S Banks, J McNiff, R Smith, M Romero, J Dunham, T Lalani, R Tant, M Cerroni, A Austin, T Warkentien, C Berjohn, K Gyben, N Kirkland, D Larson, R Maves, G Utz, G Zollicoffer, C Friend, J Modi, A Saperstein, D Tilley, M Wayman, T Burgess, L Kosh, R J O'Connell, M Simons, A Williams, A Fries, V Hogan, S Purves, I Barahona, A Ganesan, J Jefferson, L Lawson, S Loo, S Loy, M Macisaac, W Campbell, L Collins, L Housel, S Seshadri, C Spooner, D Hrncir, B McClenathan, J Brunader, C Lohsl, E Hall, L Gazlay, C Marrier, S Bedno, A Farmer, A Nivens, G Collins, M Hussain, D Turner, M Richardson, J Ritschl, A Smith, D Crosby, J Simon, A Williams, K Matthews, C Armstrong, D Parish, and J Powers, III
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
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