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Respiratory Pathogen Surveillance Trends and Influenza Vaccine Effectiveness Estimates for the 2018–2019 Season Among Department of Defense Beneficiaries

Navy Seaman Kenny Liu prepares a needle with a flu vaccination aboard the USS Gerald R. Ford in Newport News, Va., Oct. 22, 2019. The ship’s crew received flu vaccines. Navy Seaman Kenny Liu prepares a needle with a flu vaccination aboard the USS Gerald R. Ford in Newport News, Va., Oct. 22, 2019. The ship’s crew received flu vaccines.

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ABSTRACT

This report primarily focuses on the data collected and analyzed from the worldwide network of sentinel military treatment facilities chosen to participate in the Department of Defense Global Respiratory Pathogen Surveillance (DoDGRS) program. Sites that participated in the 2018–2019 DoDGRS program submitted 24,320 respiratory specimens for diagnostic testing. Clinical results showed a total of 5,968 positive influenza cases. In the beginning of the season, starting in surveillance week 48, influenza A(H1N1)pdm09 was the predominant subtype. The predominant subtype switched to influenza A(H3N2) beginning in week 6 and continued through the end of the season. Influenza B virus detection was less common during the surveillance period (i.e., 1% of total submitted specimens and 5% of total influenza detected). In addition to routine surveillance, the DoDGRS program also conducts vaccine effectiveness (VE) studies twice per year to determine interim and end of season estimates. Overall, the adjusted end of season VE for all dependents regardless of influenza type was 30% (95% CI: 22%–38%).

WHAT ARE THE NEW FINDINGS?   

DoDGRS program data showed that influenza A(H1N1)pdm09 was the dominant A subtype from October 2018 through February 2019. Influenza A(H3N2) was the predominant subtype from February through May 2019, extending this season further into the spring than prior seasons. The influenza vaccine reduced the odds of medically attended, laboratory-confirmed influenza of all types examined by 30% among all dependents.

WHAT IS THE IMPACT ON READINESS AND FORCE HEALTH PROTECTION?

The tendency for prevalent influenza viruses to be rapidly succeeded by viruses with different antigenic characteristics highlights the need for researchers to continually assess VE to help reduce influenza burden and improve military influenza vaccination policies. Future research on influenza VE in the active duty population to assess the effects of waning immunity and the impact of repeated vaccinations would inform better influenza vaccination policy.

BACKGROUND

In 1976, the U.S. Air Force initiated a global influenza surveillance network called “Project Gargle.”1 In 1997, a Presidential Decision Directive, National Science and Technology Council 7 (NSTC-7) created the Department of Defense’s (DoD’s) Global Emerging Infections Surveillance and Response System (GEIS) and formally expanded Project Gargle’s mission to become a tri-service program.1,2 Since then, GEIS has provided central coordination and financial support for Project Gargle, which was renamed the DoD Global Respiratory Pathogen Surveillance (DoDGRS) program in 2017. DoDGRS is managed by the U.S. Air Force School of Aerospace Medicine’s (USAFSAM's) epidemiology laboratory and the Defense Health Agency’s (DHA's) Air Force Satellite at Wright-Patterson Air Force Base in Dayton, OH.

The DoDGRS program consists of a network of sentinel and partner laboratories worldwide that monitor respiratory pathogen activity among military members and their beneficiaries. The operational goals of the program are to identify outbreaks, determine the incidence of influenza-like illness (ILI), characterize circulating influenza viruses, and evaluate influenza vaccine effectiveness (VE). Results from these efforts are shared with the Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and the World Health Organization (WHO) to facilitate the selection of hemispheric vaccine components. The purpose of this report is to provide an overview of the DoDGRS program and to summarize influenza activity during the 2018–2019 influenza season.

METHODS

Data collection

Each year, military treatment facilities are selected using criteria relating to geographical location to participate in the DoDGRS program.2 These sites submit 6 to 10 respiratory specimens per week from patients meeting the ILI case definition: 1) presentation within 72 hours after illness onset with a fever (i.e., = 100.5°F) and cough or sore throat or 2) physician-diagnosed ILI. Patients’ clinical histories, vaccination statuses, and demographic data are recorded by the healthcare provider or self-reported using a questionnaire. Vaccination status is obtained from the Air Force Complete Immunization Tracking Application (AFCITA) or self-reported on the DoDGRS questionnaire. Three laboratories process the specimens: Landstuhl Regional Medical Center (LRMC) for all European Command (EUCOM) sites, Brooke Army Medical Center (BAMC) for the San Antonio region, and USAFSAM for all other locations.

Laboratory testing

The gold standard and preferred method for specimen collection is the nasal wash method because of the amount of sample (i.e., 3 mL) that can be obtained.1,3 However, the USAFSAM epidemiology laboratory is validated to accept either nasal wash specimens or nasopharyngeal swabs for diagnostic testing and patient management. USAFSAM and LRMC specimens were tested via multiplex reverse transcriptase polymerase chain reaction (RT-PCR) and viral culture (USAFSAM only) to identify the presence of influenza and other respiratory pathogens. For BAMC specimens, the cobas® Liat® influenza A/B assay and/or BioFire® FilmArray® testing were utilized. Viruses from a subset of the laboratory-confirmed influenza cases were sequenced utilizing a whole genome amplification approach on the Illumina MiSeq platform and analyzed using the iterative refinement meta-assembler (IRMA) software.4–6

Statistical analysis for VE estimation

A test-negative case-control study design was used to calculate end of season VE for DoD dependents. A subset of the total cases and controls was chosen based on a period of peak influenza activity (> 10% influenza positivity rate) between 16 December 2018 and 27 April 2019 (weeks 51–17). The analysis was restricted to this time frame to minimize bias that might occur with an overrepresentation of controls that are typically seen earlier or later in the influenza season. Individuals were classified as vaccinated if at least 1 immunization occurred at least 14 days before specimen collection. If an individual’s vaccination status could not be determined, their record was not included in the analysis.

Cases were defined as individuals whose specimens were laboratory-confirmed influenza positives, and controls were persons with negative influenza test results. The influenza VE point estimates were derived from an odds ratio (OR) that compared the odds of influenza vaccination among the cases versus the controls. Crude and adjusted VE estimates were calculated as [1 - OR] x 100% along with their 95% confidence intervals (CIs) using multivariable logistic regression. The regression models were adjusted for potential confounding factors such as age group, sex, time of specimen collection, and geographical region. VE estimates with an associated 95% CI that excluded zero were considered statistically significant. The VE analyses were stratified by influenza subtype (i.e., influenza A, A(H1N1)pdm09, A(H3N2), B) and demographic population (i.e., all dependents, children, adults, elderly). Since service members are typically highly vaccinated (i.e., > 90%), they were excluded from the analyses. Severe cases were also excluded from the VE analysis because of a smaller sample size for this subgroup. While the elderly population was included in the VE analysis, this subgroup also had a smaller sample size leading to statistically insignificant results, which were not reported. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).

RESULTS

Viral surveillance

During the 2018–2019 season, the DoDGRS program received a total of 24,320 specimens from 111 locations. The demographic profile of the persons who provided specimens can be found in Table 1. Most of the specimens came from children (i.e., non-service member beneficiaries < 18 years; n=8,387), followed by service members (n=7,797), adults (i.e., spouses of service members and retirees 18–64 years; n=4,885), and the elderly (i.e., non-service member beneficiaries 65+ years; n=2,190), with 1,061 specimens from beneficiaries with an unknown age category. Table 2 shows the distribution of respiratory pathogens identified during the season. Among the submitted specimens, 5,968 tested positive for influenza (including influenza coinfections). Of these influenza positives, 1,497 were influenza A (not subtyped), 1,529 were influenza A(H1N1)pdm09, and 2,369 were influenza A(H3N2). There was low influenza B activity during this period (n=170); detected lineages included B/Victoria (n=88) and B/Yamagata (n=21).

Figure 1 shows influenza activity trends and the corresponding positivity rates. Additionally, the multiplex assay for USAFSAM and LRMC identified 4,404 specimens that were positive for a single noninfluenza respiratory pathogen, 545 noninfluenza coinfections, and 294 influenza coinfections. The noninfluenza pathogens detected were rhinovirus/enterovirus (n=1,750), coronavirus (n=821), respiratory syncytial virus (RSV) (n=613), parainfluenza (n=464), adenovirus (n=320), human metapneumovirus (n=295), Mycoplasma pneumoniae (n=63), Chlamydophila pneumoniae (n=43), and human bocavirus (n=35) (Table 2).

Genetic sequencing

From 30 September 2018 through 13 August 2019, a total of 2,711 influenza virus RNA gene sequences were either generated at USAFSAM or contributed by partner laboratories at the Armed Forces Research Institute of Medical Sciences (AFRIMS), Naval Medical Research Unit No. 2 (NAMRU-2), or the Naval Health Research Center (NHRC). In total, 1,187 (43.8%) influenza A(H1N1)pdm09, 1,304 (48.1%) influenza A(H3N2), 179 (6.6%) influenza B/Victoria lineage, and 41 (1.5%) influenza B/Yamagata lineage hemagglutinin (HA) sequences were characterized (data not shown).

All 1,187 of the influenza A(H1N1) pdm09 HA sequences were in clade 6B.1A, a subclade that is well inhibited by the 2018–2019 influenza A(H1N1)pdm09 vaccine component, A/Michigan/45/2015-like virus (clade 6B.1) (data not shown).

The 1,304 influenza A(H3N2) HA sequences belonged to clades 3C.3a (69.9%), 3C.2a1b (27.2%), 3C.2a2 (2.2%), 3C.2a3 (0.5%), or 3C.2a (0.2%) with no further subclade designation. An early season predominance of the 3C.2a1b clade was replaced by the 3C.3a clade through the remainder of the season (Figure 2). The 2018–2019 influenza A(H3N2) vaccine component, A/Singapore/INFIMH-16-0019/2016-like virus resided in the 3C.2a1 clade, which has shown a lack of protection against 3C.3a clade viruses.7

The 179 influenza B/Victoria HA sequences belonged to clade V1A.1 (24.0%) containing a 2-amino-acid deletion at positions 162–163, V1A-3Del (67.6%) containing a 3-amino-acid deletion at positions 162–164, or V1A (8.4%). Following the 2017–2018 season, there was an increase in the proportion of the V1A-3Del clade viruses (Figure 3). The 2018–2019 influenza B/Victoria vaccine component (B/Colorado/06/2017-like virus) was a V1A.1 clade, which has shown limited protection against both the V1A and V1A-3Del clade viruses.7

All 41 of the influenza B/Yamagata HA sequences were in clade Y3. This clade has changed little in the last several seasons and is the clade represented in the 2018–2019 influenza B/Yamagata vaccine component, B/Phuket/3073/2013-like virus, which is included in the quadrivalent vaccine only.

VE

During this surveillance period, there were 2,216 cases and 3,702 controls. When comparing the proportions of cases to controls, statistically significant differences were observed for the following demographic characteristics: sex, age, month of illness, vaccination status, and influenza type (Table 3). The cases and controls did not differ significantly by geographical region. The vaccination rate was approximately 58% among the cases and 68% among the controls.

A summary of the VE estimates stratified by subtype is displayed in Figure 4. Overall, the adjusted VE for all influenza types and dependents was 30% (95% CI: 22%–38%). The adjusted VE for children (aged 2–17 years) against all influenza types was 27% (95% CI: 15%–38%). The adjusted VE against all influenza types for adults (aged > 18 years) was 36% (95% CI: 23%–46%). The dependents VE was 29% (95% CI: 20%–37%) for influenza A(not subtyped), 54% (95% CI: 45%–62%) for influenza A(H1N1)pdm09, 25% (95% CI: 13%–36%) for influenza A(H3N2), and 51% (95% CI: 19%–71%) for influenza B. The highest VEs among children were for influenza A(H1N1)pdm09 (VE = 62%; 95% CI: 51%–70%) and for influenza B (VE = 56%; 95% CI: 17%–76%). The highest VEs among adults were for influenza A(H3N2) (VE = 41%; 95% CI: 23%–55%) and for influenza A(H1N1)pdm09 (VE = 39%; 95% CI: 19%–54%).

EDITORIAL COMMENT

Although the 2018–2019 influenza season was relatively moderate, it was still one of the longest seasons in nearly 10 years, lasting approximately 21 weeks.7 Data collected from the active surveillance effort conducted by the DoDGRS program showed 2 overlapping waves of influenza A activity throughout this season. In particular, influenza A(H1N1)pdm09 was the dominant subtype from October 2018 through February 2019, with the period of greatest activity occurring in week 4. The second wave was due to the emergence of influenza A(H3N2) as the predominant subtype from February through May 2019, extending this season further into the spring than prior seasons and peaking during weeks 7–11.

Based on the genetic characterization of the influenza A(H3N2) virus, multiple clades (i.e., 3C.3a, 3C.2a1b, 3C.2a2, 3C.2a3, 3C.2a) were cocirculating this season. This finding was consistent with the WHO’s determination of an increasing prevalence of different influenza A(H3N2) virus groups in some countries in the Northern Hemisphere, which led to a lower than typical VE for the influenza A(H3N2) strain.As a result, experts from the WHO influenza advisory group recommended postponing the selection of the influenza A(H3N2) vaccine component for the 2019–2020 influenza vaccine.This decision allotted time to monitor the degree of antigenic drift for the circulating influenza A(H3N2) strains and update the candidate viruses that would be included in the 2019–2020 season’s vaccine to yield a more optimal VE.8

Because of the retrospective nature of the VE analysis, there are some limitations that are inherent in the design of this observational study. Although a typical feature of influenza includes fever, some influenza cases are afebrile. This required criterion in the ILI case definition might decrease the amount of cases that can be captured and therefore reduce the precision of the influenza estimates. To address this concern, a sensitivity analysis was done for the ILI case definition, and no significant differences were found (data not shown).

Since the questionnaires contain self-reported data, results of the analysis may be incomplete or inaccurate, overestimating or underestimating the associations of interest. This potential recall bias was addressed by eliminating unknown vaccination status from the analysis and preventing nondifferential misclassification of the exposure. Additionally, electronic vaccination records with more accurate information were used to minimize this bias. Because case-control studies are also prone to selection bias, the cases and controls were selected from the same sentinel site data repository using similar inclusion criteria. Potential confounders were adjusted for in the regression models (i.e., covariates such as age, sex, geographical region, time of specimen collection) to increase the external validity of the study’s findings. Influenza outcomes were defined by a sensitive diagnostic test (i.e., RT-PCR) ensuring a precise confirmation of influenza positives, reducing outcome misclassification and any possible negative impact on the VE estimate.1 It is important to note, however, that influenza VE estimates may be affected by repeat vaccinations, timing of vaccine uptake, or a patient’s immune response. Selection of the start and end dates of the period of peak influenza activity may also have affected the VE estimates.

Results of the end of season VE analysis showed that the influenza vaccine reduced the odds of medically attended, laboratory-confirmed influenza by 30% among all dependents, offering moderate protection this season. Nonetheless, the ability of the influenza virus to rapidly change highlights the necessity for public health preparedness in the wake of emerging and reemerging infectious respiratory diseases. Thus, researchers still need to be at the forefront of VE assessments to continually reduce influenza burden and improve military influenza vaccination policies. Additionally, avenues for future research should concentrate on accurately estimating VE in the active duty population as well as evaluating the effects of waning immunity and the impact of repeated vaccinations. More research in these areas would provide important insights and contribute to better influenza vaccination policy decisions.

Author affiliations: Defense Health Agency/Armed Forces Health Surveillance Branch Air Force Satellite–U.S. Air Force School of Aerospace Medicine, Wright-Patterson Air Force Base, OH (Ms. Kersellius, Ms. DeMarcus, Lt Col Robbins); STS Systems Integration, LLC, San Antonio, TX (Ms. Kersellius, Ms. DeMarcus, Mr. Gruner, Dr. Fries); U.S. Air Force School of Aerospace Medicine epidemiology laboratory, Wright-Patterson Air Force Base, OH (Mr. Gruner, Dr. Fries).

Acknowledgments: The authors would like to thank the Department of Defense Global Respiratory Pathogen Surveillance Program, the U.S. Air Force School of Aerospace Medicine epidemiology laboratory, and the Landstuhl Regional Medical Center Laboratory and its sentinel site partners for their participation and contributions to specimen and data collection. The authors would also like to thank the Public Health Command, Europe for their valuable contributions to this work.

REFERENCES

1. DeMarcus LS, Parms TA, Thervil JW. The DoD Global, Laboratory-based, Influenza Surveillance Program: Summary for the 2013–2014 Influenza Season. MSMR. 2016;23(3):2–5.
2. Canas LC, Lohman K, Pavlin JA, et al. The Department of Defense laboratory-based global influenza surveillance system. Mil Med. 2000;165(7 suppl 2):52–56.
3. Irving SA, Vandermause MF, Shay DK, Belongia EA. Comparison of nasal and nasopharyngeal swabs for influenza detection in adults. Clin Med Res. 2012;10(4):215–218.
4. Zhou B, Lin X, Wang W, et al. Universal influenza B virus genomic amplification facilitates sequencing, diagnostics, and reverse genetics. J Clin Microbiol. 2014;52(5):1330–1337.
5. Zhou B, Donnelly ME, Scholes DT, et al. Single-reaction genomic amplification accelerates sequencing and vaccine production for classical and swine origin human influenza A viruses. J Virol. 2009;83(19):10309–10313.
6. Shepard SS, Meno S, Bahl J, Wilson MM, Barnes J, Neuhaus E. Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler. BMC Genomics. 2016;17(708).
7. Xu X, Blanton L, Elal AIA, et al. Update: Influenza activity in the United States during the 2018–19 season and composition of the 2019–20 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2019;68(24):544–551.
8. World Health Organization. Influenza. Recommended composition of influenza virus vaccines for use in the 2019–2020 northern hemisphere influenza season. Frequently asked questions to the A(H3N2) component of the recommendation. https://www.who.int/influenza/vaccines/virus/recommendations/201902_qanda_recommendation_ah3n2.pdf?ua=1. Accessed 24 September 2019.

Number of specimens submitted and percent of influenza positive by week, DoD beneficiaries, 2018–2019 influenza season

Influenza A(H3N2) clade dynamics over 2 seasons, DoD beneficiaries, October 2017–August 2019

 Influenza B/Victoria clade dynamics over 2 seasons, DoD beneficiaries, October 2017–August 2019

End of season (2018–2019) adjusted VE estimates, by influenza subtype, DoD beneficiaries, 2018–2019 influenza season

Demographic data for sources of specimens and results of routine testing, DoD beneficiaries, 2018–2019 influenza season


Distribution of respiratory pathogens, DoD beneficiaries, 2018–2019 influenza season


Demographic characteristics of the laboratory-confirmed influenza positive cases and test-negative controls used in VE analysis, 2018–2019 influenza season

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