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Influenza epidemiology and influenza vaccine effectiveness during the 2014–2015 season: annual report from the Global Influenza Hospital Surveillance Network

  • Joan Puig-Barberà1Email author,
  • Elena Burtseva2,
  • Hongjie Yu3,
  • Benjamin J. Cowling4,
  • Selim Badur5,
  • Jan Kyncl6,
  • Anna Sominina7 and
  • on behalf of the GIHSN
BMC Public HealthBMC series – open, inclusive and trusted201616(Suppl 1):757

https://doi.org/10.1186/s12889-016-3378-1

Published: 22 August 2016

Abstract

The Global Influenza Hospital Surveillance Network (GIHSN) has established a prospective, active surveillance, hospital-based epidemiological study to collect epidemiological and virological data for the Northern and Southern Hemispheres over several consecutive seasons. It focuses exclusively on severe cases of influenza requiring hospitalization. A standard protocol is shared between sites allowing comparison and pooling of results. During the 2014–2015 influenza season, the GIHSN included seven coordinating sites from six countries (St. Petersburg and Moscow, Russian Federation; Prague, Czech Republic; Istanbul, Turkey; Beijing, China; Valencia, Spain; and Rio de Janeiro, Brazil). Here, we present the detailed epidemiological and influenza vaccine effectiveness findings for the Northern Hemisphere 2014–2015 influenza season.

Keywords

InfluenzaVirusSurveillanceVaccineHospitalizationEpidemiological study

Introduction

Every year, between 5 % and 10 % of adults and 20 – 30 % of children have symptomatic influenza illness [1, 2], and 3 to 5 million individuals suffer from severe influenza, leading to 250,000 to 500,000 deaths [24]. Influenza illness can result in hospitalization and death, mainly among high-risk groups but also in a substantial proportion of previously healthy individuals [5]. In recent years, especially after the 2009 pandemic season, influenza surveillance has been expanded, as recommended by the World Health Organization (WHO), to include additional epidemiological data [6].

The Global Influenza Hospital Surveillance Network (GIHSN) is an international public-private collaboration initiated in 2012 by Sanofi Pasteur and the Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), a regional public health institution in Valencia, Spain. The aim of the GIHSN is to improve understanding of influenza epidemiology to better inform public health policy decisions. It is the first global network focusing exclusively on severe cases of influenza requiring hospitalization. The GIHSN runs a prospective, active surveillance, hospital-based epidemiological study to collect epidemiological and virological data for the Northern and Southern Hemispheres over several consecutive seasons. A standardised protocol and standard operating procedures are shared between sites allowing comparison and pooling of results [7]. The GIHSN is coordinated by FISABIO and is made up of several country sites affiliated with national health authorities. Each site coordinates several hospitals in its region. The network currently includes 27 hospitals coordinated by 7 sites in 6 countries (St. Petersburg and Moscow, Russian Federation; Prague, Czech Republic; Istanbul, Turkey; Beijing, China; Valencia, Spain; and Rio de Janeiro, Brazil).

The surveillance data collected by the GIHSN are used to describe the circulating strains related to severe disease, estimate the burden of severe influenza disease, and evaluate the benefit of influenza vaccination to prevent severe disease. Results have been published from the network’s first two seasons, 2012–2013 [5, 8] and 2013–2014 [9]. In this report, we describe the influenza epidemiology and vaccine effectiveness results from the GIHSN during the 2014–2015 influenza season. Complete data from the Southern Hemisphere was not available at the time of the meeting or during analysis and writing, so only data provided by sites in the Northern hemisphere during the 2014–2015 season are presented.

Methods

Summary of overall methodology

As described in detail elsewhere [7], patients admitted in the participating hospitals are included, after written consent, if they are residents in the predefined hospital’s catchment area, present with an acute illness possibly related to influenza, are not institutionalised, and the onset of symptoms was within 7 days of admission. Swabs are collected from patients meeting the inclusion criteria and tested by reverse transcription-polymerase chain reaction (RT-PCR) for influenza (Fig. 1). Influenza-positive samples are sub-typed by RT-PCR to identify A(H1N1)pdm09, A(H3N2), B/Yamagata-lineage, and B/Victoria-lineage strains. Vaccine effectiveness is assessed using a test-negative design in which vaccine coverage is compared between admissions with and without laboratory-confirmed influenza.
Fig. 1

Overview of the methodology used by the GIHSN

Epidemiological analysis

Epidemiological and virological data were collected from 7 coordinating sites and a total of 27 hospitals in 6 countries (Additional file 1). Briefly, eligible admissions included non-institutionalised residents in the predefined catchment areas of the participating hospitals, hospitalised in the last 48 h, and with presenting illness potentially associated with influenza (Additional file 2 and Additional file 3). The study activities were performed over influenza circulation periods defined using pre-specified criteria (Additional file 3). Nasopharyngeal swabs (all subjects), pharyngeal swabs (subjects ≥14 years) or nasal swabs (subjects <14 years) were tested by semi-quantitative RT-PCR for influenza A (subtypes H3 and H1pdm09) and B (Yamagata and Victoria lineages). The distribution of hospital admission according to RT-PCR result was described by site and risk group. Secondary outcomes included hospital admissions by subtype for influenza A(H1N1)pdm09, A(H3N2), and B-lineage, by site and risk group. The significance of differences among groups or categories was estimated by the likelihood ratio test, t-test, or nonparametric tests as required. A P-value <0.05 was considered to indicate statistical significance. To describe the major determinants for admission with influenza (vs. influenza-negative admission), a stepwise logistic regression model was fitted by including all risk factors at P < 0.2. Adjusted odds ratios (aORs) for RT-PCR-positive vs. RT-PCR-negative admissions in the presence of major risk factors of interest were estimated by multivariate logistic regression using minimal sufficient adjustment sets of covariates identified as confounders by causal diagrams. To account for the possible effect of study site, data were fitted to a random effects logistic regression model including site as a cluster variable. Likelihood ratio tests were used to check for the potential effect of clustering by site [10]. The adjusted effect of site in the probability of influenza with admission was estimated. Heterogeneity in the effects of risk factors by influenza strain and site were quantified using the I2 test. Heterogeneity was defined as an I2 > 50 % [11, 12]. Further details are provided elsewhere [5, 7, 8].

Influenza vaccine effectiveness analysis

Influenza vaccine effectiveness (IVE) was estimated as (1 ˗ OR) × 100, where the OR compared the vaccine coverage rate between influenza-positive and influenza-negative patients. Patients were considered vaccinated if they had received the current season’s influenza vaccine at least 14 days before symptom onset. The types of vaccines used at each site are summarised in Additional file 4. IVE overall (irrespective of vaccine type) was determined in patients who had been swabbed within 7 days of the onset of ILI symptoms. Records for which outcome, exposure, or confounding variables were missing were excluded from the multivariate IVE analyses. The adjusted IVE was estimated by logistic regression using a random effects model with study site as a shared parameter for the pooled analysis and including week of symptom onset as a continuous variable, and age group, sex, hospitalisation in the previous 12 months, presence of chronic conditions, and smoking habits as potential confounding factors. A P-value <0.05 was considered to indicate statistical significance. Heterogeneity in IVE estimates was assessed using the I2. Potential sources of heterogeneity, including coordinating site, age, and influenza subgroup were examined in ad-hoc analyses. Heterogeneity was defined as low if I2 statistic <25 %, moderate if 25 – 49 %, and high if ≥50 %. Further details of the methodology are described elsewhere [8].

Results

Epidemiology of influenza in the GIHSN during the 2014–2015 influenza season

Patients included in the epidemiology analysis

Twenty thousand five hundred fifty-one eligible admissions were identified between November 16, 2014 and May 23, 2015, of which 9614 met the selection criteria and were included (Table 1). Based on RT-PCR, 2177 (23 %) were positive for influenza. Major reasons for exclusion included no ILI symptoms before admission (15 %), previous admission fewer than 30 days from the current episode (13 %), admission more than 7 days after the onset of symptoms (6 %), recruitment outside periods of continuous admissions with influenza (6 %).
Table 1

Selection of patients and results of RT-PCR

 

St. Petersburg

Moscow

Czech Republic

Turkey

Beijing

Valencia

Total

Category

n

%

n

%

n

%

n

%

n

%

n

%

n

%

Screened admissions

3164

 

1934

 

123

 

1409

 

1425

 

12,496

 

20,551

 

Exclusion criteria

 Non resident

21

0.7

95

4.9

12

9.8

73

5.2

5

0.4

50

0.4

256

1.2

 Institutionalised

14

0.4

14

0.7

2

1.6

17

1.2

2

0.1

800

6.4

849

4.1

 Previous discharge <30 days

31

1.0

51

2.6

8

6.5

216

15.3

13

0.9

2283

18.3

2602

12.7

 Unable to communicate

20

0.6

47

2.4

2

1.6

125

8.9

0

0.0

782

6.3

976

4.7

 Not giving consent

100

3.2

32

1.7

14

11.4

47

3.3

15

1.1

504

4.0

712

3.5

 No ILI symptoms ≥5 years of age

19

0.6

25

1.3

1

0.8

131

9.3

18

1.3

2903

23.2

3097

15.1

 Admission within 7 days of symptoms onset

181

5.7

150

7.8

4

3.3

110

7.8

44

3.1

745

6.0

1234

6.0

 Previous influenza infection

1

0.0

0

0.0

0

0.0

7

0.5

0

0.0

1

0.0

9

0.0

 Onset of symptoms to swab >9 days

0

0.0

1

0.1

0

0.0

2

0.1

0

0.0

1

0.0

4

0.0

 Sample inadequate

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

5

0.0

5

0.0

 Sample lost

0

0.0

0

0.0

0

0.0

0

0.0

1

0.1

1

0.0

2

0.0

 Recruited outside periods with continuous influenza positive admissions

31

1.0

115

5.9

1

0.8

65

4.6

178

12.5

764

6.1

1154

5.6

Included with valid laboratory results

2715

85.8

1400

72.4

79

64.2

614

43.6

1149

80.6

3657

29.3

9614

46.8

RT-PCR result

 Influenza negative

2113

77.8

966

69.0

20

25.3

543

88.4

875

76.2

2920

79.8

7437

77.4

 Influenza positive

602

22.2

434

31.0

59

74.7

71

11.6

274

23.8

737

20.2

2177

22.6

 Subtype and lineagea

  A(H1N1)pdm09

47

7.8

30

6.9

7

11.9

26

36.6

1

0.4

10

1.4

121

5.6

  A(H3N2)

267

44.3

163

37.6

33

55.9

6

8.5

163

59.5

611

82.9

1243

57.1

  A not subtyped

48

8.0

9

2.1

2

3.3

0

0.0

0

0.0

47

6.4

106

4.9

  B/Yamagata lineage

258

42.9

175

40.3

16

27.1

0

0.0

109

39.8

65

8.8

623

28.6

  B/Victoria lineage

0

0

10

2.3

0

0

0

0.0

1

0.4

0

0.0

11

0.5

  B not subtypedb

0

0

52

12.0

2

3.4

39

54.9

0

0.0

4

0.5

97

4.5

Abbreviations: ILI, influenza-like disease; RT-PCR, reverse transcriptase-polymerase chain reaction

aBecause there were 24 mixed infections, each involving two different influenza viruses, the sum by strain may be greater than the number of patients included with lab results. Percentages are reported by total of influenza-positive cases

bFor Turkey and Valencia, all B not subtyped were assumed to be B/Yamagata lineage based on virus circulation at these sites. This assumption was not applied for Moscow because of a mixed pattern of influenza B circulation

Influenza viruses identified in admissions

In the 2177 included influenza-positive patients, A(H3N2) (n = 1243; 57 %) was the most commonly identified type of influenza, followed by B/Yamagata-lineage (n = 623; 29 %), A(H1N1)pdm09 (n = 121;6 %), A not subtyped (106; 5 %), B lineage not determined (n = 97; 5 %), and B/Victoria-lineage (n = 11; 0.5 %) (Table 1 and Fig. 2a and b). Mixed influenza infections were found in 24 cases. Influenza B lineage not determined were considered B/Yamagata-lineage for 39 cases in Turkey and four in Valencia. Due to the mixed circulation of B/Yamagata and B/Victoria lineages in Moscow, this assumption was not applied at that site to cases where B-lineage was not determined.
Fig. 2

Admissions with influenza by epidemiological week and virus type, subtype, or lineage overall and by site. B strains included B not subtyped and mixed influenza infections including influenza B but excluded B/Victoria lineage

The 2014–2015 influenza season at the GIHSN sites

Influenza was detected over a span of 27 weeks, from week 47 of 2014 to week 20 of 2015, with the peak at week 7 of 2015 (Fig. 2). The earliest start of the influenza season was reported in Beijing, where influenza-positive admissions occurred over a span of 23 weeks in two waves, the first due to A(H3N2) and the second due to B/Yamagata-lineage (Fig. 2). The latest influenza-positive admission was in St. Petersburg, where continuous weekly admissions with influenza were observed over a span of 19 weeks.

A(H3N2) was the most frequently detected influenza virus in St. Petersburg (44 % of positives), Czech Republic (56 %), Beijing (60 %), and Valencia (83 %) (Table 1). B/Yamagata-lineage was the second-most frequently detected influenza virus in St. Petersburg (43 %), Czech Republic (27 %), and Beijing (39 %). With the exception of Beijing and Turkey, A(H3N2) and B/Yamagata-lineage co-circulated at all sites (Fig. 2). In Turkey, A(H3N2) accounted for only 8.5 % of positives, and instead, B influenza viruses predominated (55 %), followed by A(H1N1)pdm09 (37 %), with co-circulation of these two viruses (Table 1 and Fig. 2).

Main characteristics of included patients

Overall, all age groups were represented. Approximately one-third of included admissions were patients less than 5 years of age, one-third were 5 to 64 years of age, and one-third were 65 years of age or older (Table 2). More than half of the included patients were male (n = 5417; 56 %). Most (n = 5867; 61 %) did not have an underlying chronic condition, and most (n = 6756; 70 %) had not been hospitalised in the 12 months before the current episode. Among the 39 % (n = 3747) of admissions in patients with underlying chronic conditions, the most frequent were cardiovascular disease (n = 1998; 21 %), chronic respiratory conditions (including chronic obstructive pulmonary disease [COPD; n = 1459] and asthma [n = 446]; 20 %), diabetes (n = 1048; 11 %), and renal disease (n = 606; 6 %). Few patients had active neoplasms (3 %), neuromuscular diseases (3 %), autoimmune diseases (2 %), liver disease (2 %), or immunodeficiency (1 %).
Table 2

Characteristics of included patients overall and by site

 

St. Petersburg

Moscow

Czech Republic

Turkey

Beijing

Valencia

Total

 

N = 2715

N = 1400

N = 79

N = 614

N = 1149

N = 3657

N = 9614

Characteristic

n

%

n

%

n

%

n

%

n

%

n

%

n

%

Age in years, median (range)

3 (0–94)

19 (0–90)

51 (19–91)

12 (0–98)

8 (0–96)

73 (0–106)

21 (0–106)

Age group

 0–1 y

714

26.3

137

9.8

0

0.0

112

18.2

76

6.6

476

13.0

1515

15.8

 2–4 y

1034

38.1

371

26.5

0

0.0

133

21.7

403

35.1

265

7.2

2206

22.9

 5–17 y

357

13.1

171

12.2

0

0.0

80

13.0

147

12.8

72

2.0

827

8.6

 18–49 y

426

15.7

632

45.1

38

48.1

38

6.2

106

9.2

221

6.0

1461

15.2

 50–64 y

110

4.1

59

4.2

14

17.7

75

12.2

131

11.4

359

9.8

748

7.8

 65–74 y

39

1.4

13

0.9

12

15.2

69

11.2

93

8.1

593

16.2

819

8.5

 75–84 y

31

1.1

11

0.8

9

11.4

80

13.0

127

11.1

969

26.5

1227

12.8

 ≥85 y

4

0.1

6

0.4

6

7.6

27

4.4

66

5.7

702

19.2

811

8.4

Sex

 Male

1489

54.8

827

59.1

40

50.6

356

58.0

696

60.6

2009

54.9

5417

56.3

 Female

1226

45.2

573

40.9

39

49.4

258

42.0

453

39.4

1648

45.1

4197

43.7

Chronic conditions

 0

2380

87.7

1246

89.0

29

36.7

182

29.6

820

71.4

1210

33.1

5867

61.0

 1

244

9.0

116

8.3

31

39.2

196

31.9

233

20.3

1026

28.1

1846

19.2

 >1

91

3.4

38

2.7

19

24.1

236

38.4

96

8.4

1421

38.9

1901

19.8

Previously hospitalised (last 12 months)

 No

1781

65.6

1123

80.2

56

70.9

341

55.5

964

85.1

2491

68.1

6756

70.4

 Yes

934

34.4

277

19.8

23

29.1

273

44.5

169

14.9

1166

31.9

2842

29.6

Underlying chronic conditions

 Cardiovascular disease

140

5.2

68

4.9

29

36.7

215

35.0

218

19.0

1328

36.3

1998

20.8

 Chronic obstructive pulmonary disease

51

1.9

19

1.4

6

7.6

153

24.9

137

11.9

1093

29.9

1459

15.2

 Asthma

60

2.2

19

1.4

3

3.8

74

12.1

8

0.7

282

7.7

446

4.6

 Immunodeficiency/organ transplant

30

1.1

0

0.0

3

3.8

48

7.8

0

0.0

25

0.7

106

1.1

 Diabetes

32

1.2

14

1.0

12

15.2

96

15.6

34

3.0

860

23.5

1048

10.9

 Renal impairment

18

0.7

26

1.9

4

5.1

61

9.9

12

1.0

485

13.3

606

6.3

 Neuromuscular disease

68

2.5

15

1.1

1

1.3

79

12.9

12

1.0

92

2.5

267

2.8

 Neoplasm

7

0.3

9

0.6

9

11.4

79

12.9

7

0.6

190

5.2

301

3.1

 Cirrhosis/liver disease

34

1.3

21

1.5

5

6.3

19

3.1

5

0.4

118

3.2

202

2.1

Autoimmune disease

13

0.5

14

1.0

4

5.1

22

3.6

0

0.0

122

3.3

175

1.8

Pregnant (women 15–45 y)

0

0.0

291

95.7

1

7.1

1

4.8

0

0.0

5

5.7

298

45.4

Obese (all ages)

263

9.7

162

11.6

12

15.2

109

17.8

155

13.5

957

26.2

1658

17.2

Outpatient consultations last 3 months

 0

1215

44.8

492

35.1

23

29.1

113

18.4

4

0.3

649

17.7

2496

26.0

 1

895

33.0

314

22.4

19

24.1

100

16.3

697

60.9

678

18.5

2703

28.1

 >1

605

22.3

594

42.4

37

46.8

401

65.3

443

38.7

2330

63.7

4410

45.9

Smoking habits (patients ≥18 y)

 Never smoker

325

53.3

345

47.9

40

50.6

135

46.7

269

51.4

1363

47.9

2477

48.9

 Past smoker

76

12.5

136

18.9

19

24.1

117

40.5

162

31

1034

36.4

1544

30.5

 Current smoker

209

34.3

240

33.3

20

25.3

37

12.8

92

17.6

447

15.7

1045

20.6

Functional status impairment (Barthel score; patients ≥65 y)

 Total (0–15)

1

1.4

0

0.0

3

11.1

6

3.4

23

10.0

166

7.3

199

7.1

 Severe (20–35)

1

1.4

0

0.0

1

3.7

3

1.7

19

8.3

71

3.1

95

3.4

 Moderate (40–55)

1

1.4

1

3.3

1

3.7

6

3.4

37

16.1

140

6.2

186

6.6

 Mild (60–90)

25

33.8

6

20.0

9

33.3

76

43.2

136

59.1

414

18.3

666

23.8

 Minimal (95–100)

46

62.2

23

76.7

13

48.1

85

48.3

15

6.5

1473

65.1

1655

59.1

Sampling time

 0–2 days

1351

49.8

655

46.8

20

25.3

125

20.4

324

28.2

896

24.5

3371

35.1

 3–4 days

915

33.7

523

37.4

26

32.9

213

34.7

382

33.2

1572

43.0

3631

37.8

 5–7 days

449

16.5

221

15.8

24

30.4

239

38.9

358

31.2

1058

28.9

2349

24.4

 8–9 days

0

0.0

1

0.1

9

11.4

37

6.0

85

7.4

131

3.6

263

2.7

Influenza vaccination ≥14 days from symptom onset

59

2.2

39

2.8

1

1.3

28

4.6

127

11.1

1759

48.1

2013

20.9

Just under half (n = 298; 45 %) of the admitted women 15–45 years of age were pregnant. Obese patients represented 17 % (n = 1658) of admissions. Among admissions in adult patients (≥18 years; n = 5066), 1045 (21 %) were current smokers, 1544 (30 %) were past smokers, and 2477 (49 %) had never smoked. Among elderly patients (≥65 years; n = 2857), 17 % (n = 480) had severe functional impairment as defined by a Barthel index <60. Finally, 2013 (21 %) admissions were in patients that had received the current season’s influenza vaccine at least 14 days before the onset of symptoms. Overall, swabs were obtained within 4 days after the onset of symptoms onset in 7002 (73 %) of included admissions.

Site-related characteristics of included patients

Patients included in St. Petersburg were younger than patients included at other sites (Table 2). The difference in age of included patients was especially marked when comparing St. Petersburg with the Czech Republic and Valencia. Patients were most frequently young adults in Moscow and the Czech Republic. Ages were homogeneously distributed in Turkey and Beijing (P = 0.9480). By contrast, in Valencia most (62 %) admissions were in elderly patients (≥65 years).

Patients without comorbidities represented 88 % of admissions in St. Petersburg, 89 % in Moscow, 71 % in Beijing, 37 % in Czech Republic, 30 % in Turkey, and 33 % in Valencia. Of the different chronic conditions, cardiovascular disease, respiratory disease, and diabetes were the most common, and their relative importance at each site corresponded to the proportion of patients with one or more underlying chronic condition.

In Moscow, among admissions in patients with known risk factors for influenza, pregnant women represented the majority of admissions (n = 291; 96 %). Obese patients represented 10 – 15 % of admissions in St. Petersburg, Moscow, the Czech Republic, and Beijing, whereas 18 % in Turkey and 26 % in Valencia were obese. The proportion of who never smoked ranged from 47 – 53 % in adult (≥18 years) admissions and was similar across sites (p = 0.1520). The overall proportion of current smokers, however, differed, with the highest rate (34 %) in St. Petersburg and Moscow (33 %), followed by Czech Republic (25 %), Beijing (18 %), Valencia (16 %), and Turkey (13 %) (p < 0.0001). For elderly adults, functional impairment status was mild or minimal in 83–97 % of included admissions at all sites except Beijing, where 30 % of admissions in elderly patients had moderate to severe functional impairment. Rates of influenza vaccination were below 5 % for all sites except Beijing (11 %) and Valencia (48 %).

Heterogeneity between sites

The proportion of samples with positive results differed between sites, from as low as 12 % for Turkey to as high as 75 % for the Czech Republic (Table 1; p < 0.0001 by test of homogeneity for equal odds). This difference persisted after excluding pregnant women and excluding the two sites with extreme results: proportions with positive results were 22 % for St. Petersburg, 31 % for Moscow, 24 % for Beijing, and 20 % for Valencia (p < 0.0014 by test of homogeneity for equal odds). After excluding pregnant women, however, proportions were homogenous in St. Petersburg, Moscow, and Beijing (p < 0.1464 by test of homogeneity for equal odds). After adjusting for sex, age, comorbidity, previous admissions, time to swab, influenza vaccination, and calendar time, the heterogeneity of aORs for a positive result were similar to the unadjusted results (Additional file 5 and Additional file 6; I2 = 96.4 %; p <0.0001).

Risk of admission with influenza according to age and sex and variability by influenza virus

Influenza positivity was related to age. Overall, influenza-positive admissions tended to be older than influenza-negative admissions (Table 3). Admissions positive for A(H1N1)pdm09 were younger than those negative for influenza, those positive for A(H3N2), and those positive for B/Yamagata-lineage. Also, admissions positive for A(H3N2) were older than influenza-negative admissions, those positives for A(H1N1)pdm09, and those positive for B/Yamagata-lineage (Table 3 and Fig. 3).
Table 3

Characteristics of included patients according to PCR result

 

Influenza-negative

Influenza-positive

A(H1N1)pdm09

A(H3N2)

B/Yamagata lineage

 

N = 7437

N = 2177

N = 115

N = 1231

N = 646

 

n

%

n

%

P vs. negative

n

%

P vs. negative

n

%

P vs. negative

n

%

P vs. negative

Age in years, median (range)

18.4 (0–106)

 

32.8 (0–100)

 

0.0001

5.6 (0–85)

 

0.0861

54.5 (0–100)

 

0.0001

26.2 (0–96)

 

0.0013

Age group

    

<0.0001

  

<0.0001

  

<0.0001

  

<0.0001

 0–1 y

1371

18.4

144

6.6

 

14

12.2

 

74

6.0

 

37

5.7

 

 2–4 y

1777

23.9

429

19.7

 

36

31.3

 

212

17.2

 

133

20.6

 

 5–17 y

547

7.4

280

12.9

 

14

12.2

 

121

9.8

 

118

18.3

 

 18–49 y

1038

14

423

19.4

 

28

24.3

 

183

14.9

 

168

26.0

 

 50–64 y

557

7.5

191

8.8

 

9

7.8

 

92

7.5

 

73

11.3

 

 65–74 y

608

8.2

211

9.7

 

5

4.3

 

153

12.4

 

45

7.0

 

 75–84 y

933

12.5

294

13.5

 

8

7.0

 

235

19.1

 

38

5.9

 

 ≥85

606

8.1

205

9.4

 

1

0.9

 

161

13.1

 

34

5.3

 

Sex

    

<0.0001

  

0.0390

  

0.0003

  

0.0040

 Male

4276

57.5

1141

52.4

 

55

47.8

 

651

52.9

 

333

51.5

 

 Female

3161

42.5

1036

47.6

 

60

52.2

 

580

47.1

 

313

48.5

 

Chronic conditions

    

0.0940

  

0.1600

  

<0.0001

  

0.004

 0

4572

61.5

1295

59.5

 

78

67.8

 

643

52.2

 

434

67.2

 

 ≥1

2865

38.5

882

40.5

 

37

32.2

 

588

47.8

 

212

32.8

 

Underlying chronic conditions

 Cardiovascular disease

1529

20.6

469

21.5

0.3210

18

15.7

0.1820

319

25.9

<0.0001

109

16.9

0.02200

 Chronic obstructive pulmonary disease

1153

15.5

306

14.1

0.0570

7

6.1

0.0001

222

18.0

0.0270

60

9.3

<0.0001

 Asthma

346

4.7

100

4.6

0.9080

5

4.3

0.8760

65

5.3

0.3440

24

3.7

0.2600

 Immunodeficiency/organ transplant

92

1.2

14

0.6

0.0130

0

0.0

0.0920

8

0.6

0.0550

6

0.9

0.4750

 Diabetes

814

10.9

234

10.7

0.7960

8

7.0

0.1480

180

14.6

<0.0001

34

5.3

<0.0001

 Renal impairment

463

6.2

143

6.6

0.5640

2

1.7

0.0200

108

8.8

0.0010

27

4.2

0.0280

 Neuromuscular disease

215

2.9

52

2.4

0.2020

8

7.0

0.0290

19

1.5

0.0040

22

3.4

0.4670

 Neoplasm

238

3.2

63

2.9

0.4660

4

3.5

0.8680

38

3.1

0.8330

17

2.6

0.4160

 Cirrhosis/liver disease

168

2.3

34

1.6

0.0390

0

0.0

0.0220

19

1.5

0.0950

11

1.7

0.3390

 Autoimmune disease

127

1.7

48

2.2

0.1360

1

0.9

0.4470

36

2.9

0.0060

8

1.2

0.3510

Pregnant (women 15–45 y)

138

33.7

160

64.8

0.0000

11

68.8

0.0050

64

55.7

<0.0001

68

70.1

<0.0001

Obese (all ages)

1300

17.5

358

16.4

0.2590

15

13.0

0.1970

223

18.1

0.5890

92

14.2

0.0320

Smoking habits (patients ≥18 y)

 Never smoked

1760

47.0

717

54.2

 

23

45.1

 

451

54.7

 

191

53.4

 

 Past smoker

1164

31.1

380

28.7

 

14

27.5

 

252

30.6

 

96

26.8

 

 Current smoker

818

21.9

227

17.1

 

14

27.5

 

121

14.7

 

71

19.8

 

Functional status impairment (Barthel score; patients ≥65 y)

 Total (0–15)

152

7.2

47

6.7

 

0

0.0

 

41

7.6

 

5

4.3

 

 Severe (20–35)

76

3.6

19

2.7

 

0

0.0

 

13

2.4

 

4

3.5

 

 Moderate (40–55)

140

6.7

46

6.6

 

0

0.0

 

32

5.9

 

11

9.6

 

 Mild (60–90)

518

24.7

148

21.1

 

1

7.1

 

111

20.5

 

32

27.8

 

 Minimal (95–100)

1214

57.8

441

62.9

 

13

92.9

 

345

63.7

 

63

54.8

 

Influenza vaccination ≥14 d since onset of symptoms

1566

21.1

447

20.5

0.5960

6

5.2

<0.0001

356

28.9

<0.0001

57

8.8

<0.0001

Not subtyped A and B were 24 patients with mixed influenza infections were not included in the analysis by strain

Fig. 3

Proportion of admissions by strain and age group

After adjusting for sex, occupational class, comorbidity, influenza vaccination, time to swab, and the clustering effect of site, heterogeneity due to strain was significant for admissions in subjects ≥5 years of age due to a decrease in aOR with age for admission with A(H1N1)pdm09 (Table 4 and Additional file 7). After excluding admissions with A(H1N1)pdm09, the aOR for admission with influenza was homogeneous for elderly patients but heterogeneous for patients 5–64 years of age (I2 = 75–77 %) due to a higher aOR for admissions with B/Yamagata-lineage than for A(H3N2) (Additional file 7).
Table 4

Subject characteristics and risk of admission with influenza

 

All admissions

Influenza-positive

Crude OR

Heterogeneity by strain (I2)a

aORb

 

N = 9164

N = 2177

Characteristic

n

n

%

Value

95 % CI

Value

95 % CI

Age

 0–1 y

1515

144

9.5

1.00

-

32.5 %

1.00

-

 2–4 y

2206

429

19.4

2.30

1.88-2.81

0.0 %

2.14

1.74-2.64

 5–17 y

827

280

33.9

4.87

3.90-6.10

73.3 %

4.34

3.42-5.51

 18–49 y

1461

423

29.0

3.88

3.16-4.77

59.2 %

3.11

2.49-3.90

 50–64 y

748

191

25.5

3.26

2.57-4.14

72.5 %

4.08

3.11-5.36

 65–74 y

819

211

25.8

3.30

2.62-4.17

67.8 %

4.99

3.76-6.64

 75–84 y

1227

294

24.0

3.00

2.42-3.72

62.4 %

4.51

3.43-5.92

 ≥85

811

205

25.3

3.22

2.55-4.07

71.2 %

4.79

3.59-6.40

Sexc

 Male

5417

1141

21.1

1.00

-

0.0 %

1.00

-

 Female

4197

1036

24.7

1.24

1.13-1.37

0.0 %

1.21

1.09-1.34

 Female non-pregnant

3899

876

22.5

1.11

1.00-1.22

0.0 %

1.10

0.99-1.23

Other risk factors (excludes pregnant women)

 Comorbidityd

3709

856

23.1

1.15

1.04-1.27

62.8 %

1.48

1.30-1.69

 Cardiovascular disease

1996

468

23.4

1.17

1.04-1.33

22.2 %

1.47

1.25-1.72

 Chronic obstructive pulmonary disease

1458

306

21.0

1.02

0.88-1.17

57.6 %

1.39

1.15-1.68

 Asthma

440

96

21.8

1.07

0.84-1.35

0.0 %

1.37

1.04-1.80

 Immunosuppression

106

14

13.2

0.58

0.33-1.03

0.0 %

0.76

0.40-1.46

 Diabetes

1048

234

22.3

1.10

0.94-1.29

0.0 %

1.36

1.10-1.70

 Renal disease

588

129

21.9

1.08

0.88-1.32

48.2 %

1.23

0.95-1.59

 Neuromuscular

167

52

31.1

0.93

0.68-1.26

38.9 %

1.13

0.80-1.58

 Neoplasm (active)

301

63

20.9

1.01

0.76-1.35

55.6 %

1.29

0.92-1.81

 Liver disease

200

33

16.5

0.76

0.52-1.11

50.8 %

0.79

0.52-1.21

 Autoimmune disease

161

39

24.2

1.22

0.85-1.77

0.0 %

1.44

0.95-2.18

 Obesee

1620

337

20.8

1.0

0.9-1.2

0.0 %

0.87

0.73-1.03

Pregnancyf

298

160

53.7

3.45

2.23-5.34

0.0 %

2.08

1.43-3.03

 Associated comorbidity

38

26

68.4

7.07

3.09-16.18

0.0 %

4.29

2.65-6.94

 No comorbidity

260

126

48.5

3.05

2.08-4.47

0.0 %

1.80

1.22-2.66

aStrains considered: A(H3N2), A(H1N1)pdm09 and B/Yamagata

bMinimal sufficient adjustment sets for estimating the exposure or risk factor effect on the risk of admission with influenza vs. all included admissions without underlying conditions or pregnant

cFemale or female non-pregnant vs. male. aORs were adjusted for age, occupational social class group, underlying comorbidity, obesity, influenza vaccination, time to swab, calendar time, and site as a clustering factor

dOne or more underlying conditions or individual comorbidities vs. no comorbidity. aORs were adjusted for sex, occupational social class group, obesity, influenza vaccination, time to swab, calendar time, and site as a clustering factor

eaOR adjusted for sex, age, occupational social class group, influenza vaccination, time to swab, calendar time, and site as a clustering factor

fWomen 15-45 years of age included in Moscow, St. Petersburg, Czech Republic, Turkey and Valencia. aOR adjusted for smoking habits, time to swab, calendar time, comorbidity, and site as a clustering factor. For results stratified by comorbidity, aORs were adjusted by the same covariates and were estimated taking into account the interaction between pregnancy and comorbidity

Female patients had a higher risk than male patients of being influenza-positive (aOR, 1.21 [95 % CI, 1.09-1.34]), irrespective of strain (I2 = 0 %). However, after excluding pregnant women, the risk was more similar for males and females (aOR, 1.10 [95 % CI, 0.99–1.23]) (Table 4).

Risk of admission with influenza according presence of comorbidity

Similar proportions of influenza-positive admissions (882/2177; 41 %) and influenza-negative admissions (2865/7437; 39 %) had one or more chronic underlying condition (p = 0.0940) (Table 3). After excluding pregnant women, 42 % of influenza-positive admissions had comorbidity compared to 39 % of influenza-negative admissions (p = 0.006) (data not shown). The aOR for admission with influenza was 1.5 (95 % CI, 1.3–1.7) for patients with comorbidities, although the values were heterogeneous by strain (I2 = 63 %) (Table 4) due to a higher aOR for admission with A(H3N2) or B/Yamagata-lineage in patients with comorbidities compared to patients with no underlying conditions (Additional file 8).

Irrespective of the involved strain (I2 = 22 %), the risk of admission with influenza was significantly increased in patients with cardiovascular disease (aOR = 1.5 [95 % CI, 1.3–1.7), asthma (1.4 [95 % CI, 1.0–1.8]), or diabetes (1.4 [95 % CI, 1.1–1.7]) (Table 4, Fig. 4, and Additional file 9). The aOR was heterogeneous for the risk of admission with influenza in patients with COPD (aOR 1.4 [95 % CI, 1.2–1.7]; I2 = 58 %) due to lower aOR for admission with A(H1N1)pdm09 (Additional file 9). Point values for aORs were above 1.0 for admission with influenza for patients with renal, neuromuscular, or autoimmune disease, but 95 % CIs overlapped 1.0. In patients with active neoplasms, the overall aOR for influenza-positive admission was heterogeneous and not significant (I2 = 56 %; aOR = 1.3 [95 % CI, 0.9–1.8]), although for B/Yamagata-lineage, the risk was significantly elevated (aOR = 2.2 [95 % CI, 1.1–4.1]) (Additional file 9).
Fig. 4

Adjusted odds ratio (aOR) and number of admissions with influenza according to comorbidity. CVD, cardiovascular disease. COPD: chronic obsructive pulmonary disease

Risk of admission with influenza according pregnancy

A total of 298 included admissions were pregnant women 15–45 years of age, 291 of whom were included in Moscow, one in the Czech Republic, one in Turkey, and five in Valencia (Table 2). No pregnant women were included in Beijing. Non-pregnant women in this age group accounted for another 359 included admissions, of which 197 were in St. Petersburg, 13 in Moscow, 13 in the Czech Republic, 20 in Turkey, 33 in Beijing, and 83 in Valencia (data not shown).

The probability of laboratory-confirmed influenza was higher in included pregnant women than included same age non-pregnant women (54 % vs. 24 %; p < 0.0001; data not shown). After taking into account clustering by site (and not considering data from Beijing), the crude OR of admission with influenza was 3.5 (95 % CI, 2.2–5.3) (Table 4). This crude estimated OR was higher in pregnant women with associated comorbidity (OR 7.1 [95 % CI, 3.1–16.2]), with moderate evidence of an interaction between comorbidity and pregnancy before adjustment (p = 0.0659) and a significant interaction after adjustment (p < 0.0001). Taking into account the modifying effect of associated comorbidity, the aOR for admission with influenza in pregnant women was 4.3 (95 % CI, 2.7–6.9) in presence of associated comorbidity and 2.1 (95 % CI, 1.4–3.0) for pregnant women with no comorbidity. In both cases, the values were homogenous (I2 = 0 %) for A(H3N2), A(H1N1)pdm09, and B/Yamagata-lineage infections.

The probability of admission with influenza was higher in all three trimesters for pregnant women without associated comorbidities than for non-pregnant women in the same age group without comorbidity. In pregnant women with comorbidities, the risk of admission with influenza was highest in the first trimester (Fig. 5 and Additional file 10).
Fig. 5

Predicted probability of admission with influenza in non-pregnant 15 – 45 years old women and by pregnancy trimester in same age pregnant women

Risk of admission with influenza and complications by strain

Intensive care unit (ICU) admissions, extracorporeal membrane oxygenation, and mechanical ventilation were more frequent for influenza-negative than for influenza-positive admissions (p ≤ 0.002), whereas rates of in-hospital death were similar (p = 0.3460) (Table 5). By strain, the point estimate of rates of ICU admission and extracorporeal membrane oxygenation were higher in admissions with A(H1N1)pdm09, although differences were not significant. In contrast, rates of in-hospital death were significantly higher in admissions with A(H3N2) (p = 0.0080). Less than 4 % of admissions in these categories experienced a severe outcome. Finally, length of stay did not differ between influenza-positives and influenza-negative admissions for influenza overall or between strains (Table 5).
Table 5

Influenza severity and complications by RT-PCR result

Category

Influenza-negative

Influenza-positive

P-value influenza-negative vs. positive

A(H1N1)pdm09

A(H3N2)

B/Yamagata lineage

P-value for distribution by strain

N = 7437

N = 2177

N = 115

N = 1231

N = 646

n

%

n

%

n

%

n

%

n

%

Severity indicator

 Intensive care unit admission

184

2.5

31

1.4

0.0020

4

3.5

15

1.2

9

1.4

0.2400

 Mechanical ventilation

123

1.7

20

0.9

<0.0001

1

0.9

14

1.1

4

0.6

0.5230

 Extracorporeal membrane oxygenation

184

2.8

25

1.3

0.0020

3

2.6

9

0.8

9

1.7

0.1600

 Death during hospitalisation

131

1.8

32

1.5

0.3460

1

0.9

26

2.1

3

0.5

0.0080

 Length of stay (days), median (interquartile range)

6

(4-9)

6

(4-8)

0.0612

6

(3-8)

6

(3-8)

6

(4-8)

0.2835

Pulmonary complications

    

<0.0001

      

<0.0001

 None

1939

26.1

1212

55.7

 

53

46.1

697

56.6

353

54.6

 

 Pneumonia

1545

20.8

364

16.7

 

18

15.7

214

17.4

109

16.9

 

 COPD exacerbation

265

3.6

87

4.0

 

3

2.6

66

5.4

15

2.3

 

 Respiratory failure

55

0.7

32

1.5

 

0

0.0

23

1.9

5

0.8

 

 Asthma exacerbation

28

0.4

12

0.6

 

0

0.0

11

0.9

1

0.2

 

 pulmonary collapse

5

0.1

1

0.0

 

0

0.0

1

0.1

0

0.0

 

 Acute respiratory distress syndrome

7

0.1

2

0.1

 

0

0.0

2

0.2

0

0.0

 

 Bronchiolitis

416

5.6

201

9.2

 

15

13.0

91

7.4

75

11.6

 

 Upper respiratory infection

3172

42.7

266

12.2

 

26

22.6

126

10.2

88

13.6

 

Metabolic failure

    

0.4690

      

0.3530

 Acute renal failure

87

1.2

32

1.5

 

1

0.9

24

1.9

5

0.8

 

 Diabetic coma

4

0.1

2

0.1

 

0

0.0

1

0.1

0

0.0

 

 Fluid/electrolyte/acid-base/balance disorders

80

1.1

29

1.3

 

1

0.9

19

1.5

7

1.1

 

Cardiovascular events

    

0.3390

      

<0.0001

 None

6335

85.2

1883

86.5

 

107

93.0

991

80.5

612

94.7

 

 Acute myocardial infarction

8

0.1

5

0.2

 

0

0.0

5

0.4

0

0.0

 

 Acute heart failure

1

0.0

1

0.0

 

0

0.0

1

0.1

0

0.0

 

 Cardiac arrest

4

0.1

3

0.1

 

0

0.0

3

0.2

0

0.0

 

 Malignant hypertension

37

0.5

10

0.5

 

0

0.0

9

0.7

1

0.2

 

 Any cardiovascular condition

1050

14.1

275

12.6

 

8

7.0

222

18.0

33

5.0

 

Systemic inflammatory response syndrome, shock, or disseminated intravascular coagulation

76

1.0

12

0.6

0.0320

2

1.7

9

0.7

1

0.2

0.0810

Neurologic events

 No

7423

99.8

2173

99.8

0.3140

114

99.1

1228

99.8

646

100.0

0.1249

 Altered mental status

10

0.1

4

0.2

 

1

0.9

3

0.2

0

0.0

 

 Convulsions

4

0.1

0

0.0

 

0

0.0

0

0.0

0

0.0

 

Major discharge diagnoses

    

<0.0001

      

<0.0001

 Influenza

124

1.7

1266

58.2

 

76

66.1

603

49.0

456

70.6

 

 Pneumonia

1807

24.3

223

10.2

 

10

8.7

145

11.8

59

9.1

 

 Other respiratory disease

3653

49.1

290

13.3

 

20

17.4

188

15.3

57

8.8

 

 Cardiovascular

603

8.1

117

5.4

 

0

0.0

105

8.5

7

1.1

 

 Other

1250

16.8

281

12.9

 

9

7.8

190

15.4

67

10.4

 

Exacerbation of chronic obstructive pulmonary disease, respiratory failure, exacerbation of asthma, and bronchiolitis were more frequently reported for influenza-positive admissions than for influenza-negative admissions (Table 5). These were associated with A(H3N2), except in the case of bronchiolitis, where the proportions for admission with all three strains (A(H3N2), A(H1N1)pdm09, and B/Yamagata-lineage) were higher than the proportion for influenza-negative admissions. Cardiovascular events were more frequently reported for admissions with influenza A(H3N2) than for admissions with influenza A(H1N1)pdm09 or B (OR 1.3 [95 % CI, 1.1–1.6]; p = 0.0004; data not shown), whereas, shock was more frequent in admissions with influenza A(H1N1)pdm09 (adjusted p < 0.0001; Table 5).

Figure 6 shows the estimated marginal probabilities by strain and age for severe outcomes after adjusting by sex, comorbidity, calendar time, age, and clustering by site. We found several non-significant associations: A(H1N1)pdm09 was associated with intensive care unit admission and shock; A(H3N2) was associated with an increased probability of COPD exacerbation, respiratory failure, cardiovascular complications, and death; B/Yamagata-lineage was related to respiratory failure; and all three strains were related to death at both extremes of age (Fig. 6). We found similar non-significant associations for complications when influenza-negative admissions were included (Additional file 11).
Fig. 6

Predicted probability of severe outcome by strain (not subtyped, mixed influenza with influenza infections and B/Victoria lineage excluded)

Influenza vaccine effectiveness in the GIHSN during the 2014–2015 influenza season

Patients included in the influenza vaccine effectiveness analysis

After applying exclusions related to vaccine contraindication (egg allergy and <6 months of age), 8455 specimens collected from November, 2014 through May, 2015 were included in the IVE analyses. Of all collected specimens, 2027 (24 %) were positive for influenza, of which 1165 (57 %) were positive for A(H3N2), 104 (5 %) for A(H1N1)pdm09, and 625 (31 %) for B Yamagata-lineage (Table 6). Overall, 22 % (n = 446) of influenza-positive admissions and 24 % (n = 1556) of influenza-negative admissions were vaccinated (p = 0.042) (Table 7). The proportion of patients vaccinated with the seasonal influenza vaccine in 2014–2015 ≥ 14 days before symptom onset was 3 % in St. Petersburg (n = 43) and Moscow (n = 30), 5 % in Turkey (n = 22), 11 % (n = 94) in Beijing, and 54 % (n = 1367) in Valencia (data not shown).
Table 6

IVE for all cases and for targeted groups only by age and strain

   

Influenza-positive

Influenza-negative

Crude IVE

Adjusted IVE

Population

Strain

Age

Total

Vaccinated

Total

Vaccinated

Percent (95 % CI)

P interaction

Percent (95 % CI)

P interaction

Overall

Any

Any

2027

446

6428

1556

-1 (-17, 12)

 

22 (8, 33)

 
 

<65 y

1334

78

4299

289

-4 (-36, 20)

0.090

-5 (-38, 20)

0.054

 

≥65 y

693

368

2129

1267

21 (5, 34)

24 (9, 37)

A(H3N2)

Any

1165

356

6428

1556

-6 (-24, 10)

 

20 (4, 33)

 
 

<65 y

630

50

4299

289

-15 (-59, 17)

0.036

-16 (-64, 17)

0.031

 

≥65 y

535

306

2129

1267

24 (7, 37)

25 (8, 39)

A(H1N1)

Any

104

7

6428

1556

25 (-85, 69)

 

27 (-82, 71)

 
 

<65 y

91

3

4299

289

16 (-173, 74)

0.996

21 (-161, 76)

0.993

 

≥65 y

13

4

2129

1267

47 (-128, 88)

59 (-83, 91)

B/Yamagata

Any

625

57

6428

1556

16 (-17, 39)

 

31 (2, 52)

 
 

<65 y

509

20

4299

289

7 (-51, 42)

0.266

29 (-17, 58)

0.273

 

≥65 y

116

37

2129

1267

38 (-2, 62

33 (-12, 61)

Targeted groups only

Any

Any

1670

425

5077

1462

13 (-2, 26)

 

23 (8, 35)

 
 

<65 y

977

57

2948

195

-21 (-70. 14)

0.037

-12 (-58, 20)

0.019

 

≥65 y

693

368

2129

1267

26 (16, 42)

28 (14, 41)

H3N2

Any

994

344

5077

1462

13 (-4, 28)

 

22 (5, 36)

 
 

<65 y

459

38

2948

195

-12 (-65, 24)

0.051

-20 (-80. 21)

0.030

 

≥65 y

535

306

2129

1267

27 (11, 41)

28 (11, 42)

H1N1

Any

84

6

5077

1462

44 (-58, 80)

 

46 (-52, 81)

 
 

<65 y

71

2

2948

195

33 (-198, 85)

0.793

39 (-167, 86)

0.770

 

≥65 y

13

4

2129

1267

47 (-128, 88)

50 (-111, 89)

B/Yamagata

Any

486

49

5077

1462

21 (-18, 46)

 

30 (-5, 53)

 
 

<65 y

370

12

2948

195

-8 (-105, 44)

0.139

8 (-79, 53)

0.250

 

≥65 y

116

37

2129

1267

38 (-2, 62)

33 (-12, 60)

Abbreviation: IVE influenza vaccine effectiveness

Table 7

Characteristics of patients included in the primary analysis by vaccination status

Risk variables

Category

Unvaccinated

Vaccinated

P value

n

%

N

%

Number of patients, n (%)

Controls

4872

75.5

1556

77.7

0.042

 

Cases

1581

24.5

446

22.3

 

Age (y)

Median (range)

14.6 (0.8-84.0)

78.7 (9.0-91.9)

<0.001

Age group, n (%)

6–11 mo

496

7.7

3

0.1

 
 

1–4 y

2120

32.9

49

2.4

 
 

5–17 y

712

11.0

102

5.1

 
 

18–49 y

1347

20.9

75

3.7

 
 

50–64 y

591

9.2

138

6.9

 
 

65–74 y

388

6.0

416

20.8

 
 

75–84 y

512

7.9

704

35.2

 
 

≥85 y

287

4.4

515

25.7

 

Female, n (%)

-

2825

43.8

843

42.1

0.188

Comorbidities, n (%)

None

4505

70

366

18.3

<0.001

 

1

1077

16.7

647

32.3

 
 

>1

871

13.5

989

49.4

 

Pregnant, n (%)

-

294

4.6

3

0.1

<0.001

Obesity, n (%)

-

982

15.2

564

28.2

<0.001

Morbid obesity, n (%)

-

86

1.3

54

2.7

<0.001

Previous hospitalisation within 12 months, n (%)

-

1887

29.2

761

38

<0.001

GP visit within 3 months, n (%)

None

1815

28.1

343

17.1

<0.001

 

1

2027

31.4

389

19.4

 
 

>1

2606

40.4

1272

63.5

 

Smoking, n (%)

Current

1527

23.7

225

11.2

<0.001

 

Past

1069

16.6

742

37.1

 
 

Never

3856

59.8

1035

51.7

 

Functional impairment in ≥65 y, n (%)

None or minimal

619

54.2

1021

62.4

<0.001

 

Mild

324

28.4

330

20.2

 
 

Moderate

95

8.3

88

5.4

 
 

Severe

32

2.8

62

3.8

 
 

Total

72

6.3

123

7.5

 

Sampling interval (days)

Median (range)

3 (1-7)

4(1-7)

<0.001

Sampling interval, n (%)

≤4 d

3703

57.4

990

49.5

<0.001

 

5-7 d

2587

40.1

936

46.8

 
 

8-9 d

163

2.5

76

3.8

 

Site, n (%)

St. Petersburg

2138

33.1

59

2.9

<0.001

 

Moscow

1306

20.2

39

1.9

 
 

Turkey

503

7.8

26

1.3

 
 

Beijing

996

15.4

127

6.3

 
 

Valencia

1510

23.4

1751

87.5

 

Vaccinated, n (%)

 

In 2012–2013

473

7.5

1471

73.3

<0.001

 

In 2013–2014

513

8.1

1722

87.1

<0.001

Overall, 1709 of 2002 (85 %) influenza vaccinations among study patients were both self-reported and confirmed from registries. Self-report captured 156 of 2002 vaccinations (8 % overall, 67 % in Moscow, 8 % in Turkey, 1 % in Beijing, and 7 % in Valencia; data not shown). Another 137 patients (7 % overall, 12 % in St. Petersburg, 42 % in Turkey, and 7 % in Valencia; data not shown) with vaccination records failed to self-report vaccination.

The proportion of participants with comorbidity was significantly higher in vaccinated than in non-vaccinated admissions (82 % vs. 30 %, p < 0.001) (Table 7). Vaccination was also more common among elderly (median age = 79 years for vaccinated patients vs. 15 years for non- vaccinated patients, p < 0.001), obese patients (28 % obese for vaccinated patients vs. 15 % for non-vaccinated patients, p < 0.001), elderly patients with impairment or minimal functional impairment (28 % impaired for vaccinated patients vs. 15 % for non-vaccinated patients, p < 0.001), patients with outpatient visits (83 % for vaccinated patients vs. 72 % for non-vaccinated patients, p < 0.001), and patients admitted to a hospital in the previous 12 months (38 % for vaccinated patients vs. 29 % for non-vaccinated patients, p < 0.001) (Table 7). Three (0.1 %) pregnant women had received the current season’s vaccine.

Most patients vaccinated in 2014–2015 reported prior vaccination: 87 % of vaccinated patients had received the 2013–2014 vaccine (p < 0.001) and 73 % had received the 2012–2013 vaccine (p < 0.001) (Table 7). Both the 2011-2012 and 2012–2013 vaccines were received by 90 % (26/29) of cases and 77 % (89/116) of controls (p = 0.12).

Influenza vaccine effectiveness

Against all-age influenza-related hospitalisation, the overall crude IVE was −1 % (95 % confidence interval [CI], −17–12), and the adjusted IVE was 22 % (95 % CI, 8–33) (Table 6). Age at admission, presence of comorbidities, and degree of functional impairment were the covariates with the largest confounding effect on crude IVE (data not shown), raising the crude IVE with adjustment.

The adjusted IVE for patients of all ages was higher against influenza B (31 % [95 % CI, 2–52]) than for influenza A(H3N2) (20 % [95 % CI, 4–33]) and influenza A(H1N1)pdm09 (27 % [95 % CI, −82–71]) (Table 6), although confidence intervals overlapped (I2 for adjusted IVE across strains = 0 %, p = 0.762; data not shown).

Age-specific estimates suggested that vaccination against any influenza was less effective in patients <65 years of age (IVE [95 % CI] = −5 % [−38–20]) than in patients ≥65 years of age (IVE = 24 % [95 % CI, 9–37]) (P value for effect modification of age = 0.054). This pattern of lower IVE in the younger patients was consistent across strains, but only age-specific estimates for A(H3N2) were significantly different (Table 6).

Estimates were similar when the analyses were restricted to patients belonging to the target group for vaccination (crude IVE against overall influenza for all ages = 13 % [95 % CI, −2–26], adjusted IVE = 23 % [95 % CI, 8–35]) (Table 6).

IVE estimates were consistently higher for recipients of the 2012–2013 influenza vaccine, the 2013–2014 influenza vaccine, or both vaccines than for recipients of only the current season’s vaccine, although confidence intervals overlapped (Additional file 12).

Statistical heterogeneity across sites in the estimates of IVE against influenza-related hospitalisation was relatively low, with site-specific adjusted point estimates ranging from -27 – 35 % [I2 = 0 %; P = 0.835) (Additional file 13).

Sensitivity analyses were performed to assess the effects of excluding pregnant women, participants vaccinated within 14 days before symptom onset, and without medical vaccination records. In all cases, IVE estimates remained similar to those of primary analysis (Additional file 14). Further sensitivity analyses using various statistical methods to account for potential data clustering by site showed consistent results, with no evidence of heterogeneity (I2 = 0 %) in estimates of IVE across methods (Additional file 15).

Discussion

According to data collected by active surveillance within the GIHSN sites, the 2014–2015 influenza season was characterised by a predominance of A(H3N2) and B/Yamagata-lineage, and to a lesser extent, A(H1N1)pdm09, while B/Victoria-lineage was relatively rare. Reports of severe influenza, defined as hospitalisation with laboratory (i.e., PCR)-confirmed influenza, spanned 6 months and affected all ages, although influenza-related admissions were most common in older individuals. Among patients with laboratory-confirmed influenza, those with A(H1N1)pdm09 were younger than those with A(H3N2) or B/Yamagata-lineage, whereas those with B/Yamagata-lineage were most frequently young and middle-aged adults. This pattern of influenza circulation is consistent with that reported by the WHO [13]. Likewise, the age distribution of the A(H1N1)pdm09, A(H3N2) and B/Yamagata-lineage strains agrees with others’ reports [14, 15].

According to our data, comorbidity increased the risk of admission with influenza, irrespective of the strain involved. This was also the case for pregnant women. Furthermore, the combination of pregnancy and comorbidity increased the risk of admission several-fold, suggesting an interaction. Remarkably, however, nearly 60 % of eligible admissions with influenza were patients without known risk factors.

The probability of ICU admission and shock were higher in patients infected with A(H1N1)pdm09 than with other strains. Also, A(H3N2) infection was associated with respiratory failure and cardiac complications, whereas B/Yamagata-lineage was associated with an increased probability of respiratory failure. Influenza infection overall was associated with in-hospital death at both age extremes. These findings agree with other reports [1517], although there may be differences in the absolute percentage of admissions with influenza in patients with comorbidity, patterns of severity, lengths of hospital stay, rates of ICU admission, use of supportive measures, or estimates of in-hospital death rates [15, 18, 19].

Although vaccination coverage was low at the participating sites (2.8–48 %; average 20.9 %), we found that vaccination conferred a low to moderate protective effect (adjusted IVE = 22 %). This protective effect was greater for adults ≥65 years of age than for adults <65 years of age and was greater for B/Yamagata-lineage than for A(H3N2).

The low influenza vaccine effectiveness for the 2014–2015 season is similar to others’ reports and appears to be due mostly to a mismatch between the main A(H3N2) circulating strain and the vaccine strain [2023]. Across all strains, the IVE was lower in young patients, although only age-specific estimates for A(H3N2) were significantly different due to few cases of B/Yamagata-lineage and A(H1N1)pdm09 and a higher IVE in patients vaccinated during the 2012–2013, 2013–2014, or both seasons than in those vaccinated during the 2014–2015 season, a finding also reported by others [24]. This lower IVE in young patients, however, contrasts with previous reports where the opposite was found [25]. Thus, there appears to be variability in the interference or protection conferred by vaccination in previous seasons. This could be explained by the differences between the various strains circulating in different seasons and their distance from the vaccine strains, combined with inhibition of the immunological response when the vaccine strains are similar to those in previous seasons’ vaccines [26].

Limitations and considerations

Our results are to be interpreted with caution due to the heterogeneity and bias of multi-centric observational studies. We assumed heterogeneity in the circulating strains, socio-demographic diverse populations observed, their health care seeking behaviour, the characteristics of the different health care systems involved, the types of participating hospitals, and by calendar time along the season. We took account of this heterogeneity by thoroughly describing the season, the sites, and included admissions, as well as by quantifying the heterogeneity of our estimates. In this way, we are able to visualise the relative impact of the different influenza strains on diverse risk factors, including age, comorbidity, pregnancy, and obesity [12]. Furthermore, we restricted our analysis to periods with influenza circulation [27], took into account risk by calendar date [28], as well as the clustering effect of site [10] by adjusting and modelling and, finally, compared PCR-detected influenza-positive admissions with influenza-negative admissions. We consider this a reasonable approach for describing the effect of influenza in individuals according to their risk profile [29]. In addition, to reduce bias and to allow us to describe the severe consequences of community-acquired influenza, we accepted only data from patients admitted within 7 days of onset of ILI symptoms and for whom swabbing was performed within 48 h of admission.

Even with a large dataset as the one accrued annually by the GIHSN sites, small numbers are a limitation. Splitting the data by strain and risk group can decrease group sizes, so that sufficient power is available only for detecting large differences (i.e., OR ≥2). This limitation can be only dealt with by increasing the number of participating sites and by pooling data across influenza seasons. In fact, the GIHSN continues to grow, and data pooling across seasons is underway.

Most hospital studies rely on the criteria of the physician providing care for influenza confirmation and employ historical database searching [15, 17, 18, 3032]. This combined with different case definitions and laboratory methods can complicate comparisons between sites and seasons and between different studies. Our approach of using active surveillance, a shared core protocol, and PCR confirmation of influenza avoids these limitations. This approach has very recently begun to be employed by others and for other respiratory viruses [33].

Conclusions

This report describes the results from the GIHSN during the 2014–2015 influenza season that were presented at the 2015 GIHSN Annual Meeting. During the 2014–2015 influenza season, the network included 27 hospitals in six countries (Russian Federation, Czech Republic, Turkey, China, Spain, and Brazil). This offered us the opportunity to describe the characteristics of severe disease related to influenza by time, person, and strain and to describe IVE across a wide geographical area in the Northern Hemisphere.

We found that influenza is associated with severe outcomes during an extended period in the Northern Hemisphere and that comorbidity and pregnancy were significant risk factors for severe influenza illness. The distribution and impact of the three influenza virus types (A(H1N1)pdm09, A(H3N2), and B) were similar to others’ reports. An important finding was that approximately 60 % of influenza-related hospital admissions were in healthy subjects with no known comorbidity.

Our results support the current WHO recommendations on the use of influenza vaccine [4], although for the 2014–2015, IVE was low due to a significant mismatch between the circulating and vaccine viruses. We also found that IVE was affected by age and the circulating strain. These findings highlight the need to develop vaccines that are more effective and cover a broader spectrum of influenza viruses.

Abbreviations

AOR, adjusted odds ratio; CI, confidence interval; GIHSN, Global Influenza Hospital Surveillance Network; IVE, influenza vaccine effectiveness; OR, odds ratio; RT-PCR, reverse transcription-polymerase chain reaction

Declarations

Acknowledgments

The authors thank Dr. Phillip Leventhal (4Clinics, Paris, France) for medical writing, which was funded by Sanofi Pasteur.

Members of the GIHSN as of October 19, 2015

Olga Afanasieva (Research Institute of Influenza, St. Petersburg, Russian Federation), Veronica Afanasieva (Research Institute of Influenza, St. Petersburg, Russian Federation), Meral Akcay Ciblak (National Influenza Reference Laboratory Capa-Istanbul, Istanbul, Turkey), F Aktas (Faculty of Medicine Gazi University, Ankara, Turkey), Selim Badur (National Influenza Reference Laboratory Capa-Istanbul, Istanbul, Turkey), Ángel Belenguer-Varea (Hospital de La Ribera, Alzira, Spain), S Borekci (Cerrahpaşa Faculty of Medicine, Istanbul University, Istanbul, Turkey), Fernando Boza (Hospital Quinta D’Or, Rio de Janeiro, Brazil), Elena Bursteva (D.I. Ivanovsky Institute of Virology FGBC “N.F. Gamaleya FRCEM” Ministry of Health, Moscow, Russian Federation), Zhanna Buzitskaya (Research Institute of Influenza, St. Petersburg, Russian Federation), Braulia Caetano (Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil), Jian Cai (Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China), B Çakir (Department of Public Health, Faculty of Medicine, Sihhiye, Ankara, Turkey), Mario Carballido-Fernández (Hospital General, Castellón, Spain), Empar Carbonell-Franco (Hospital Arnau de Vilanova, Valencia, Spain), Concha Carratalá-Munuera (Universidad Miguel Hernández, San Juan de Alicante, Spain), S Çelebi (Uludağ University Faculty of Medicine, Bursa, Turkey), C Chai (Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China), Ivamara Changas de Lima Porto de Paula (Universidade Federal do Ceará, Fortaleza, Brazil), Enfu Chen (Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China), Ben Cowling (School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China), Yunjie Cui (Changping District Hospital, Beijing, China), DB Deniz (Dr. Siyami Ersek Gögüs Kalp ve Damar Cerrahisi Egitim ve Arastirma Hastanesi, Istanbul, Turkey), Elena Dondurei (Research Institute of Influenza, St. Petersburg, Russian Federation), H Dong (Ningbo Center for Diseases Prevention and Control, Ningbo, China), X Dong (The First Peoples’ Hospital of Huzhou, Huzhou, China), Mine Durusu (Hacettepe University, Ankara, Turkey), Clotilde El Guerche-Séblain (SANOFI Pasteur, Lyon, France), Fernanda Enda-Moura (Universidade Federal do Ceará, Fortaleza, Brazil), A Eren-Şensoy (Dr. Siyami Ersek Göğüs Kalp ve Damar Cerrahisi Eğitim ve Araştırma Hastanesi, Istanbul, Turkey), Artem Fadeev (Research Institute of Influenza, St. Petersburg, Russian Federation), Luzhao Feng (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China), Shuo Feng (School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region), Raimundo César (Ferreira de Silva Filho, Universidade Federal do Ceará, Fortaleza, Brazil), Patricia Fisch (Hospital Nossa Senhora da Conceição, Brazil), Ekaterina Garina (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), S Gencer (Dr. Lütfi Kırdar Kartal Training and Research Hospital, Istanbul, Turkey), Vicente Gil-Guillén (Hospital de Elda, Elda, Spain), Alexa Go (Research Institute of Influenza, St. Petersburg, Russian Federation), Vitaly Gonchar (Research Institute of Influenza, St. Petersburg, Russian Federation), Ekaterina Golovacheva (Research Institute of Influenza, St. Petersburg, Russian Federation), Mikhail Grudinin (Research Institute of Influenza, St. Petersburg, Russian Federation), M Hacımustafaoğlu (Uludağ University, Bursa, Turkey), S Hancerli (Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey), Martina Havlickova (National Institute of Public Health, Prague, Czech Republic), Kristyna Herrmannova (Hospital Na Bulovce, Prague, Czech Republic), L Huang (Ningbo Women and Children Hospital, Ningbo, China), Hui Jiang (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China), Dennis Ip (School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region), Helena Jirincova (National Institute of Public Health, Prague, Czech Republic), Lucie Jurzykowska (National Institute of Public Health, Prague, Czech Republic), Lidiya Kisteneva (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), Ludmila Kolobukhina (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), Andrey Komissarov (Research Institute of Influenza, St. Petersburg, Russian Federation), Radka Kralova (National Institute of Public Health, Prague, Czech Republic), Kirill Krasnoslobotsev (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), Jan Kyncl (National Institute of Public Health, Prague, Czech Republic), Xavier Labrador (FISABIO-Salud Publica, Valencia, Spain and Consorcio de Investigación Biomédica de Epidemiología y Salud Pública, Spain, Instituto Carlos II, Madrid, Spain), Chao Li (Huairou District Center for Diseases Prevention and Control, Beijing, China), Xiangxin Li (Chanping District Hospital, Beijing, China), Ramón Limón-Ramírez (Hospital de la Plana, Vila-real, Spain), Jianhua Liu (The First Hospital in Huairou District, Beijing, China), Mari Carmen (Llopis Garcia, FISABIO-Salud Pública, Valencia, Spain), Cédric Mahé (SANOFI Pasteur, Lyon, France), Zdenka Mandakova (National Institute of Public Health, Prague, Czech Republic), L Merkulova (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), Sevim Mese (Istanbul University, Istanbul, Turkey), Ainara Mira Iglesias (FISABIO-Salud Pública, Valencia, Spain), Evgenia Mukasheva (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), Angels Natividad Sancho (FISABIO-Salud Pública, Valencia, Spain), Lucia Nováková (Hospital Na Bulovce, Prague, Czech Republic), Elena Obraztsova (Research Institute of Influenza, St. Petersburg, Russian Federation), Ludmila Osidak (Research Institute of Influenza, St. Petersburg, Russian Federation), Maria del Carmen (Otero-Reigada Hospital Universitario y Politécnico La Fe, Valencia, Spain), S Özer (Dr. Lütfi Kırdar Kartal Training and Research Hospital, Istanbul, Turkey), L Ozisik (Hacettepe University, Ankara, Turkey), Valentina Picot (Fondation Mérieux, Lyon, France), Maria Pisarev (Research Institute of Influenza, St. Petersburg, Russian Federation), Florence Pradel (Fondation Mérieux, Lyon, France), Jitka Prochazkova (National Institute of Public Health, Prague, Czech Republic), Joan Puig-Barberá (FISABIO-Salud Pública, Valencia, Spain), Ying Qin (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China), Sonia Raboni (Hospital de Clínicas/Universidade Federal do Paraná, Curritiba, Brazil), Hana Roháčová (Hospital Na Bulovce, Prague, Czech Republic), Elena Rozhkova (Research Institute of Influenza, St. Petersburg, Russian Federation), Sa Li (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China), MI Sadikhova (Research Institute of Influenza, St. Petersburg, Russian Federation), Germán Schwarz-Chavarri (Hospital General de Alicante, Alicante, Spain), Marilda Siqueira (FIOCRUZ, Brazil), Elizaveta Smorodintseva (Research Institute of Influenza, St. Petersburg, Russian Federation), Anna Sominina (Research Institute of Influenza, St. Petersburg, Russian Federation), Kirill Stolyarov (Research Institute of Influenza, St. Petersburg, Russian Federation), Vera Sukhovetskaya (Research Institute of Influenza, St. Petersburg, Russian Federation), G Sun (Beijing Huairou Hospital, Beijing, China), Y Tang (Changping District Center for Diseases Prevention and Control, Beijing, China), Miguel Tortajada-Girbés (Hospital Doctor Peset, Valencia, Spain), Svetlana Trushakova (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), José Tuells (Hospital Universitario del Vinalopó, Elche, Spain), Tatiana Tumina (Research Institute of Influenza, St. Petersburg, Russian Federation), R Vartanyan (D.I. Ivanovsky Institute of Virology, Moscow, Russian Federation), Lubov Voloshuk (Research Institute of Influenza, St. Petersburg, Russian Federation), Quanyi Wang (Beijing Center for Disease Prevention and Control, Beijing, China), D Wen (Huzhou Center for Diseases Prevention and Control, Huzhou, China), Peng Wu (School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region), Wen Xiao (The First Hospital in Huairou District, Beijing, China), Peng Yang (Beijing Center for Disease Prevention and Control, Beijing, China), Marina Yanina (Research Institute of Influenza, St. Petersburg, Russian Federation), Bo Yi (Ningbo Center for Diseases Prevention and Control, Ningbo, China), Hongjie Yu (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China), Kubra Yurtcu (National Influenza Reference Laboratory Capa-Istanbul, Istanbul, Turkey), Pavel Zarishnyuk (Research Institute of Influenza, St. Petersburg, Russian Federation), S Zhang (The Third Hospital of Yinzhou District, Ningbo, China), Yi Zhang (Beijing Center for Disease Prevention and Control, Beijing, China), Tiebiano Zhang (The First Hospital in Huairou District, Beijing, China), Jiandong Zheng (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing), Zhibin Peng (Key Laboratory of Surveillance and Early-warning on Infectious Disease, Division of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China).

Funding

The study and its publication was funded by Sanofi Pasteur and the participating institutions.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its appendices.

Authors’ contributions

All authors participated in the collection and analysis of data, preparation of the manuscript, and approval of the final version.

Competing interests

The authors’ institutions received funding from Sanofi Pasteur for the conduct of this trial. The authors declare no other competing interests related to this article.

Ethics approval and consent to participate

The protocol used by the GIHSN was approved by each site’s Ethics Research Committee. All patients provided written informed consent.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Foundation for the Promotion of Health and Biomedical Research in the Valencia Region FISABIO – Public Health
(2)
D.I. Ivanovsky Institute of Virology FGBC “N.F. Gamaleya FRCEM” Ministry of Health of Russian Federation
(3)
Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention
(4)
School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
(5)
National Influenza Reference Laboratory, Istanbul Faculty of Medicine, Istanbul University
(6)
National Institute of Public Health (NIPH)
(7)
Research Institute of Influenza

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Copyright

© The Author(s). 2016

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